{"as_of":"2026-08-08T19:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4701562d6348089a61957083a2723b360b43f5a95c6ae4726fc060fafac99324","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:50:16.102887Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.17526/citation-record","integrity":"/paper/2507.17526/integrity","json":"/paper/2507.17526/citation-record.json","paper":"/paper/2507.17526"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.11171","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:19.921907Z","title":"Prediction of building power consumption using transfer learning-based reference building and simulation dataset","venue":null,"work_id":"7ceacd63-a03a-4cd6-91cc-ddf5b7e87c57","year":2022},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.788269Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:6856710642429af295f5555594f793ff7a47b08064b518760a9f9ba298eff506","observation_id":"1ea8ed9d-a894-40b8-b88d-5eb47c4fcc4b","resolution":{"observed_at":"2026-08-06T14:50:19.937352Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/9948141","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:19.811314Z","title":"DigitalTwinforHVACLoadandEnergyStoragebasedona Hybrid ML Model with CTA-2045 Controls Capability, in: 2022 IEEE Energy Conversion Congress and Exposition (ECCE), pp","venue":null,"work_id":"f4d19eca-f204-4ea6-853d-41a312d60fec","year":2022},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.794495Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:f7d313635e36f9c204f74a9ab3eac4a425251a709efb25602476d0933d7e8371","observation_id":"3b4695dd-80b4-4ffb-a7f0-8c3295081f85","resolution":{"observed_at":"2026-08-06T14:50:19.822564Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.11552","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:19.692810Z","title":"Probabilistic indoor temperature forecasting: A new approach using bernstein- polynomial normalizing flows","venue":null,"work_id":"b491311f-a79b-4aee-af62-0f8ffeeb5757","year":2025},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.802985Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:e94532d2c955feb8b77af503cea4d45394686d221f6d93b7f23dc8689091e9cc","observation_id":"e6310bae-3cc4-4a50-a7d0-207492435105","resolution":{"observed_at":"2026-08-06T14:50:19.706005Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.264305Z","title":"Identifying suitable models for the heat dynamics of buildings","venue":null,"work_id":"65e4e42b-fefb-4d74-a3ed-48f2d7b0174e","year":2011},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.809573Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:950b9389c97c9cec23d64f009e1ea97a97654cea9f1c5808f1d186b554190c26","observation_id":"73cdb926-ba1b-44a9-bc93-323bd84dfb08","resolution":{"observed_at":"2026-08-06T14:50:20.273526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.218862Z","title":"Pattern Recognition and Machine Learning","venue":null,"work_id":"e7b2bcb4-3fb3-4ce0-9a3a-1ec637cc63c8","year":null},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.822203Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:fa6887bcf645bca807d4d6d4281ab6f93c27c38754bc5d468b41d078de361c63","observation_id":"72abd05c-c68a-4dfa-8e6b-bb42992ca7b1","resolution":{"observed_at":"2026-08-06T14:50:20.225157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:15.828389Z","title":"Quantifying Uncertainty with Conformal Prediction for Heating and Cooling Load Forecasting in Building Performance Simulation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.828389Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:22aa82901f574fdd862df30f592b667b5a5317a7d0a8d0b1d560c53d67ae72c6","observation_id":"0544eff7-74eb-4d4c-b90b-7b6e7e9e04d9","resolution":{"observed_at":"2026-08-06T14:50:15.828389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.11834","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:19.563864Z","title":"Probabilistic electric load forecasting through Bayesian Mixture Density Networks","venue":null,"work_id":"8e3c7ba2-6fc6-4224-a22c-cb7891601627","year":2022},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.835409Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:dfca65e66ac84b66f46d350104e26d5f88ccfed4c2c3fb1235ef6eb00f26cce0","observation_id":"e2d9efa2-e11c-40c4-99f4-0fc792122851","resolution":{"observed_at":"2026-08-06T14:50:19.574461Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1177/1744259117701893","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.309678Z","title":"Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus","venue":null,"work_id":"322a53ba-4702-497e-add8-c89675c3c7de","year":2017},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.840902Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:36114aadd8cfae3bc049f458a4cc2bbd0b1e57131afdf39c6a34dfdc32927d60","observation_id":"7581c236-4d11-45d2-a436-6902df4a7e89","resolution":{"observed_at":"2026-08-06T14:50:16.315795Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.10162","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:19.426773Z","title":"A hybrid-model forecasting framework for reducing the building energy performance gap","venue":null,"work_id":"73ddc0de-8ea1-419c-986c-e1e95551ef46","year":null},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.847502Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:42c259345ea6f0503fcd919f5861014bc99bd09e2d1ad53a9f97f836f77a6f05","observation_id":"91ecabf0-32f8-43eb-a64c-34c211af9569","resolution":{"observed_at":"2026-08-06T14:50:19.448593Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.11014","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:19.284751Z","title":"Physics-informed neural networks for building thermal modeling and demand response control","venue":null,"work_id":"98d4c520-981e-414c-a98c-dc7cc8d2227a","year":2023},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.854366Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:5d19c7c32ee50bd5eac0416f4177bdafa59254f0978353dfa394b32159f70d7c","observation_id":"55678db2-deb5-4bd4-917d-d6ac256f72f0","resolution":{"observed_at":"2026-08-06T14:50:19.296722Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.191902Z","title":"Context-Aware Urban Energy Efficiency Optimization Using Hybrid Physical Models, in: Climate Change AI, Climate Change AI","venue":null,"work_id":"dae32d3f-341f-4204-919d-e08a7eb7b738","year":2020},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.860179Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:f5cdc350802f2c53bbac07d6eb9c26b7ebf3b0b6d867b5f81c42e71dcf0b1465","observation_id":"ea2223e7-b32a-460e-8370-3e9380c495f2","resolution":{"observed_at":"2026-08-06T14:50:20.201087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:15.865995Z","title":"Review of data-driven energy modelling techniques for building retrofit","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.865995Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:d45c08e3e0278d4d7c86b1106b5886b1e7bb2491b795fefbe789fb39ea65f471","observation_id":"bfe1c8ad-5f4a-4156-bf99-2ddd48050e2a","resolution":{"observed_at":"2026-08-06T14:50:15.865995Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.165546Z","title":"CVXPY: A Python-embedded modeling language for convex optimization","venue":null,"work_id":"d7e4a739-31ef-46a3-a1d3-a9182a6068d1","year":2016},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.871998Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:ed0a4347818dcdbdc9852ea7b963e6996e6676ddf064a8933c2aafe4c671a157","observation_id":"b4edf252-e7bd-48ff-aeeb-b36132400754","resolution":{"observed_at":"2026-08-06T14:50:20.171549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/9893988","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:19.078706Z","title":"Machine Learning & Uncertainty Quantification: Application in Building Energy Consumption, in: 2022 Annual Reliability and Maintainability Symposium (RAMS), pp","venue":null,"work_id":"640c44ee-3582-4e7d-a03d-b93b3da10f99","year":2022},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.877000Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:8bd01a11775843757ec79049deb760d7a20a358332ba55eb7b55ee81ffe7a149","observation_id":"e258f908-e8b0-4251-8ef6-9e8cdd03aec1","resolution":{"observed_at":"2026-08-06T14:50:19.091585Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/2629674","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.289946Z","title":"Modeling the Thermal Dynamics of Buildings: A Latent- Force-Model-BasedApproach","venue":null,"work_id":"aac13111-e4f2-4eeb-9ba7-bf860302cd97","year":2015},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.882666Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:6e6c2bf0356dfd82de39d45e5b22d71c44c22c1eb24f2d9d4de36039786a3300","observation_id":"4a4bce79-c05b-4a68-b4d3-d14ed2e63969","resolution":{"observed_at":"2026-08-06T14:50:16.296126Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.139180Z","title":"Adaptive conformal inference under distribution shift","venue":null,"work_id":"23b9aec4-2704-425b-8550-1639168604e9","year":2021},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.892175Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:80f32b4002b36c64f806cbb73afdf88a0fedef89b22ca5c3178060a85ac900c5","observation_id":"b64cd8e6-5ebd-4f4c-82de-66f98e781ac1","resolution":{"observed_at":"2026-08-06T14:50:20.146734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.11885","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:18.946284Z","title":"Physics informed neural networks for control oriented thermal modeling of buildings","venue":null,"work_id":"fc23d672-cb02-435e-93ad-d0c2d1dfc91b","year":2022},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.899157Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:8c79b9625e5025a8c5cda5ace2f6d5328a6dd14e282605ed173b97921050872f","observation_id":"43b35133-ada9-4af2-9f73-cd4f3df13ce4","resolution":{"observed_at":"2026-08-06T14:50:18.963340Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1493.2022","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:18.814890Z","title":"Uncertainty quantification and sensitivity analysis of energy consumption in substation buildings at the planning stage","venue":null,"work_id":"6407e2cc-0fd2-4bc1-a9c2-319c06fb16a0","year":2023},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.905779Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:331357841d953b2a7f0a2c157ef26d4659bd4c3207da92cfc26b97c663aeff51","observation_id":"f13b5696-b320-4855-b7be-bb96a1e58416","resolution":{"observed_at":"2026-08-06T14:50:18.826066Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.115123Z","title":"Quantile regression","venue":null,"work_id":"fcdbf967-8725-433d-b3f8-62e6b7e10987","year":2001},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.911951Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:33cda6e1d13e6f803d9c315a79be802ed51601bc070b7772a0a495485410a56d","observation_id":"8de5d15b-07fd-47a9-92b7-039b888a9194","resolution":{"observed_at":"2026-08-06T14:50:20.120551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2504.03350","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:18.123445Z","title":"Bayesian lstm for indoor temperature modeling","venue":null,"work_id":"2f4c98dd-2559-4d9e-8b90-bc69933dcdcb","year":2025},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.922021Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:7d326b64f366928e00ff4f5d4f3ee33a714043cff391a8beb9dec572ac1ef2a3","observation_id":"1ca8862b-90c7-4d8d-9094-8830f446aa1f","resolution":{"observed_at":"2026-08-06T14:50:18.134050Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:15.929290Z","title":"Gaussian process modeling for measurement and verification of building energy savings","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.929290Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:98f93defcb94ec919ced599053b1cae3db2bae3be85921446f91f9320a1d27de","observation_id":"fe12bb25-1cd0-49cc-869d-75a87eb371b1","resolution":{"observed_at":"2026-08-06T14:50:15.929290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s12273-018-0444-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.254702Z","title":"Building simulation: Ten challenges","venue":null,"work_id":"4ba92ea7-ebf6-4d4c-b9a5-e40679a17370","year":2018},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.937573Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:0297127a3c964ce7a2c5186332f7291cdcaf4d7f3d9dfff5948d7ad5efda9e4c","observation_id":"7050875b-ceaa-41b1-8537-dc03a9e6cafb","resolution":{"observed_at":"2026-08-06T14:50:16.261916Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"3459.33635","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:17.998243Z","title":null,"venue":null,"work_id":"0e441ae9-ecc3-4c2f-8001-220186b5b5ef","year":2019},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.943287Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:6ccf87f000bb0978723a27cbd550a2f4783e37284f4356fb89868327e27e1c01","observation_id":"ac0c2c4a-0036-43b4-be1e-5debd7061879","resolution":{"observed_at":"2026-08-06T14:50:18.007229Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.094760Z","title":"Buildings - Energy System","venue":null,"work_id":"dfd181a9-844d-497e-a02e-a620c8d5a69a","year":2023},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.949808Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:7169697bdba2685ded4a5353eac8efb4239c355f54c6cb216404d66ae4b664c8","observation_id":"462f1203-4be3-4efd-ac52-4100a4ff1af5","resolution":{"observed_at":"2026-08-06T14:50:20.100911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:15.955601Z","title":"quantile-forest: A python package for quantile regression forests","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.955601Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:efc7dbf92e2f1f219c43261ccbf6e08fcf87a7342d9fb5e6ce600866c02704b8","observation_id":"e1d2c19f-ddfe-4ef8-9ce6-3ebd949b4016","resolution":{"observed_at":"2026-08-06T14:50:15.955601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.46855/energy-proceedings-10382","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.225493Z","title":"Benchmarking HVAC controller performance with a digital twin, in: Energy Proceedings, pp","venue":null,"work_id":"4a05e82e-b30a-4a20-af04-58d74bd284c9","year":null},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.961409Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:6367e2c112203137117394982b8cdce8d00235502458bfec878e3171e834ff60","observation_id":"733cb855-a7df-4223-8ee3-4a5c95d79851","resolution":{"observed_at":"2026-08-06T14:50:16.230976Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.01055","last_updated":"2025-04-23T06:45:11Z","snapshot_observed_at":"2026-08-07T00:56:04.472847Z","submitted_at":"2024-11-01T21:56:39Z","title":"Combining Physics-based and Data-driven Modeling for Building Energy Systems","version":2},"cited_work":{"arxiv_id":"2411.01055","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.01055","snapshot_observed_at":"2026-08-06T14:50:17.889092Z","title":"Combining Physics-based and Data-driven Modeling for Building Energy Systems","venue":"eess.SY","work_id":"79871ae2-d951-4fc0-a327-2191304a3d14","year":2024},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.969597Z"},"links":{"cited_paper":"/paper/2411.01055","citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:96fa750e56218bc74cad3200116abf28dfe018e24ca3137872aa2703f1d5bb75","observation_id":"f510ef84-3215-4797-921c-d6d9b5647841","resolution":{"observed_at":"2026-08-06T14:50:17.898836Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:15.975444Z","title":"Distribution-Free Predictive Inference for Regression","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.975444Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:c3aa2d368988dacb35dd020b4452f3df952b95d426acc56f9d246324e33f1dbb","observation_id":"70255e4d-7a8d-4534-9650-1adacb79f349","resolution":{"observed_at":"2026-08-06T14:50:15.975444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:15.980724Z","title":"Energy-saving potential benchmarking method of office buildings based on probabilistic forecast","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.980724Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:2c6d0c13fa6d8dae156a4160170d0fbd5fe3c7566e7da3fbbf21d18ea78e5464","observation_id":"26a71ea9-06c7-47f4-80dc-f64d178c4dd4","resolution":{"observed_at":"2026-08-06T14:50:15.980724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.12516","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:17.630068Z","title":"A review of physics-informed machine learning for building energy modeling","venue":null,"work_id":"d050937f-32b8-43b5-a90e-a45de897aa0d","year":2025},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.986766Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:238dcd9c193475c60aedb1b4246d63b169f2fd51988d06e396bfae7690034af4","observation_id":"d33caee0-e4d0-4336-ba70-8b2991e9a32b","resolution":{"observed_at":"2026-08-06T14:50:17.642366Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.enbuild.2018.01.039","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.206334Z","title":"AhybridapproachtothermalbuildingmodellingusingacombinationofGaussianprocessesandgrey-box models","venue":null,"work_id":"2854821b-a844-4019-a0e0-2194a51dab5a","year":2018},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.993037Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:6562d19ad7ea558b81f05d04dcf95dbfe27025efefd32c52ae801281c879bbed","observation_id":"5e47f795-5f98-4245-a1cf-db108f58de58","resolution":{"observed_at":"2026-08-06T14:50:16.211507Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.075632Z","title":"Quantile regression forests","venue":null,"work_id":"2102fe85-ee55-40c7-a22c-9a09f13f0032","year":2006},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.998953Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:b0bc4585f9faeb32bff1ea94ad65ef28572595a196a204f43a0af38cf4ffaaab","observation_id":"b8699f24-9953-4a13-a566-e0494c95e946","resolution":{"observed_at":"2026-08-06T14:50:20.082144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.scs.2019","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.186431Z","title":"Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range","venue":null,"work_id":"fab8ae9a-4de4-47ba-97b2-3149e234838f","year":2020},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.004442Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:eac6479a196e39e8fbb8463ab50c9698a1ed23af68267eaebe5d51fc5b701ad6","observation_id":"0eb8eb10-e77a-4be2-b7de-7308181b556a","resolution":{"observed_at":"2026-08-06T14:50:16.192702Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.1109","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:17.504424Z","title":null,"venue":null,"work_id":"51576b1b-1ae3-4282-a2ac-c55601f01f59","year":2024},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.010543Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:adfd46e341806d5bb5da4622adc7b83e16bb96e5b402edf82b61031dce2902ee","observation_id":"b067205f-3948-4cc1-87d9-cf555ba57d63","resolution":{"observed_at":"2026-08-06T14:50:17.515455Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.12585","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:17.386074Z","title":"Weighted aggregated ensemble model for energy demand management of buildings","venue":null,"work_id":"3bcea214-cf6f-4234-94a1-888bc206059f","year":2023},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.016490Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:f6d01b11972978e9fd81042bbfeb4b3c77144318d08c35907767021bbb04fdc7","observation_id":"9f2b6fad-e9b7-464d-a509-388a2fe05b09","resolution":{"observed_at":"2026-08-06T14:50:17.397976Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.051531Z","title":"Pytorch: An imperative style, high-performance deep learning library, in: Advances in Neural Information Processing Systems 32","venue":null,"work_id":"3b722e9b-c48a-47b0-a7cc-37dc51a43dbe","year":2019},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.026066Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:43bbd461a18911ec84c45767eec6a70571626723b7f17a7f2e63743c37acfc4d","observation_id":"be88b338-6ba0-4366-ac63-29bf143264e3","resolution":{"observed_at":"2026-08-06T14:50:20.060155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.034031Z","title":"Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.034031Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:69cf57480d5e18aa8d9a18881f0078b7b53c77383733d9de3bc8df1aac96983d","observation_id":"7702de20-437c-44c0-988a-412f8aa445a5","resolution":{"observed_at":"2026-08-06T14:50:16.034031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3989/id.55380","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.160003Z","title":"NEST – una plataforma para acelerar la innovación en edificios","venue":null,"work_id":"9f52a8dc-1d1d-45ae-86f4-99ee4a9f7694","year":2018},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.043819Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:4d3a07a97c34821d7b9478be1d4c1a862c24ee6bdac23d87882f38232ac4705a","observation_id":"af0a4a79-ebd3-43f8-864f-26c55e615f77","resolution":{"observed_at":"2026-08-06T14:50:16.167785Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.030033Z","title":"Conformalized quantile regression","venue":null,"work_id":"586d88cd-cb24-4e8a-8e56-8521c437cd13","year":2019},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.049905Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:38bd739c86661315ed5f16c58195ead304a1c607ac51c822351fc3700f545eaa","observation_id":"4d013c07-d134-4dfb-bf32-9f500ba579b0","resolution":{"observed_at":"2026-08-06T14:50:20.036971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.11424","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:17.150669Z","title":"Building consumption anomaly detection: A comparative study of two probabilistic approaches","venue":null,"work_id":"9f13f760-c678-4c5f-9039-0a853297752e","year":2024},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.056841Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:f2c2c4f0e7c0000052a99af7fe4b050c1ebc601776561e5d89097741cea5fec7","observation_id":"9ee2d282-3c07-45e2-a727-3e62d61378f2","resolution":{"observed_at":"2026-08-06T14:50:17.162019Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.062538Z","title":"Department of Energy, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.062538Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:61aaf67de565a4af763d98a041e58fadc5f3f9938a58aa99075ec5f515c717fd","observation_id":"8d71b413-db3e-447b-baea-0f620378a817","resolution":{"observed_at":"2026-08-06T14:50:16.062538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.067668Z","title":"Algorithmic Learning in a Random World","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.067668Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:49b09aa7d967272b09a09db875ca3f60dd9fbf2b9b13decdc59683cc6472a968","observation_id":"7e825261-2381-49d3-b137-bf863f07167c","resolution":{"observed_at":"2026-08-06T14:50:16.067668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.007756Z","title":null,"venue":null,"work_id":"da1707ca-e7bc-465f-a356-e3add966f50a","year":2014},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.073921Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:a02ecb19b80dc50ebe7b98994be9dbfc6d47fe37ebc941807b89233fb9c9e8d7","observation_id":"9f408fa6-4143-499b-ab6e-317c65dd23d9","resolution":{"observed_at":"2026-08-06T14:50:20.014113Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.egyai","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.853816Z","title":"Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models","venue":null,"work_id":"3753b203-8307-4572-8e15-019ef3a3a822","year":2021},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.079435Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:72bcd624deeb5d6c55188e16023728ab2c0802772f609293fb2083922700aa50","observation_id":"d7be5ace-6948-460e-b028-bd6996446070","resolution":{"observed_at":"2026-08-06T14:50:16.860152Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:19.983718Z","title":"A decision-theoretic approach to interval estimation","venue":null,"work_id":"b473ed50-8146-4836-953f-6eb5b788eafc","year":1972},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.085040Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:c91e75075a2b7705183251d59bc2c5ec09cc3eccd90b3d9881d79ad6a07341ec","observation_id":"69c75d70-61e9-406a-a562-752eb9d9e674","resolution":{"observed_at":"2026-08-06T14:50:19.990997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:19.952597Z","title":"Conformal prediction interval for dynamic time-series, in: Meila, M., Zhang, T","venue":null,"work_id":"17113bcc-05d2-4808-bd7c-5ff400a83299","year":2021},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.091217Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:28a9976cc8cc85d82b3af06a09f2cfcc50d1fa2539d5af77eb466d5eacfdfe41","observation_id":"b51da6a8-f8a0-4639-ac59-fcff778cdcbe","resolution":{"observed_at":"2026-08-06T14:50:19.968262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.11581","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.608401Z","title":null,"venue":null,"work_id":"e74f7c6e-5c2f-4599-8cac-f5501385cb89","year":2025},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.096406Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:a06fccb62b8fc60e30e234ea62dfd86b3abb62d41dab12e46d31f58383a0957a","observation_id":"0ef0b38b-eb79-4462-9b08-11ab9d45504c","resolution":{"observed_at":"2026-08-06T14:50:16.618822Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.10795","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:16.487382Z","title":"Stateoftheartreviewonmodelpredictivecontrol(MPC)inHeatingVentilationandAir-conditioning(HVAC) field","venue":null,"work_id":"d6865895-650c-4c7c-a718-8db555e2e19d","year":2021},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:16.102887Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:059aa3434bd71f5fa441aab618632ab741485f539fc5b19a83e42ac88498948a","observation_id":"b6dbb68e-487d-4921-acf3-d4e00a7b0d84","resolution":{"observed_at":"2026-08-06T14:50:16.499412Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:50:20.237975Z","title":null,"venue":null,"work_id":"76b1f0b5-19d2-4c3f-8bc5-b2d128222ca9","year":2011},"citing_paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling","version":1},"reference_index":1522,"source":"pdf_text","source_observed_at":"2026-08-06T14:50:15.815829Z"},"links":{"citing_paper":"/paper/2507.17526"},"observation_digest":"sha256:0aaef0509c2115988b694e5423bca2c38863fc387e2fbc6780f3c8d42073b0e7","observation_id":"3d620027-a209-46dc-972c-12d6ff4c1391","resolution":{"observed_at":"2026-08-06T14:50:20.249604Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.17526","last_updated":"2025-07-23T14:07:33Z","latest_version":1,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-08T14:33:54.716481Z","submitted_at":"2025-07-23T14:07:33Z","title":"Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":4,"metadata_mismatch":11,"parse_uncertain":0,"unresolved":9,"verified_exact":13,"verified_fuzzy":12},"total_outbound_references":49},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2507.17526."}