{"as_of":"2026-08-15T19:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b9a9e3392a725be52f1a1ffb9254e151444b8aff4ae7c53a5c6a0fb648494a71","coverage":[{"denominator":6,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:52:46.058642Z","state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2507.07016/citation-record","integrity":"/paper/2507.07016/integrity","json":"/paper/2507.07016/citation-record.json","paper":"/paper/2507.07016"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.34048","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:52:46.295140Z","title":"Moosmann, H","venue":null,"work_id":"d73ca9f8-f813-4309-b62e-97e5d8b7af35","year":2024},"citing_paper":{"arxiv_id":"2507.07016","last_updated":"2025-07-09T16:45:33Z","snapshot_observed_at":"2026-08-06T18:46:59.459082Z","submitted_at":"2025-07-09T16:45:33Z","title":"On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T18:52:46.030208Z"},"links":{"citing_paper":"/paper/2507.07016"},"observation_digest":"sha256:70f3890227884c38d49a95361eb49ef931e7dc01a780d2e79106dcb196877c30","observation_id":"26e89ecd-db15-45ac-b2f1-a88d477083be","resolution":{"observed_at":"2026-08-06T18:52:46.306149Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T18:52:46.037126Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.07016","last_updated":"2025-07-09T16:45:33Z","snapshot_observed_at":"2026-08-06T18:46:59.459082Z","submitted_at":"2025-07-09T16:45:33Z","title":"On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T18:52:46.037126Z"},"links":{"citing_paper":"/paper/2507.07016"},"observation_digest":"sha256:1bb745b51604b3d2c293571b64f0f33f61c2766f9c1084b4cdbe9c18fec2ae84","observation_id":"d7193ff4-ebda-414e-b21c-8a11e1c9d406","resolution":{"observed_at":"2026-08-06T18:52:46.037126Z","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":"2026-08-06T18:52:46.367737Z","title":"On-Device Training Under 256KB Memory,","venue":null,"work_id":"89c50153-aaad-42e5-85a4-fff4e41503fa","year":2022},"citing_paper":{"arxiv_id":"2507.07016","last_updated":"2025-07-09T16:45:33Z","snapshot_observed_at":"2026-08-06T18:46:59.459082Z","submitted_at":"2025-07-09T16:45:33Z","title":"On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T18:52:46.042893Z"},"links":{"citing_paper":"/paper/2507.07016"},"observation_digest":"sha256:3ba62234e9fd8319d92353cd6e4e026d40a058bbff75704d4b7c58f33784221f","observation_id":"ec0c67c4-6f40-4ccd-80ab-4eb1b2b7a5e5","resolution":{"observed_at":"2026-08-06T18:52:46.372633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T18:52:46.352346Z","title":"Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point,","venue":null,"work_id":"e9d0fd49-4375-4a3d-875f-83a88134140a","year":2024},"citing_paper":{"arxiv_id":"2507.07016","last_updated":"2025-07-09T16:45:33Z","snapshot_observed_at":"2026-08-06T18:46:59.459082Z","submitted_at":"2025-07-09T16:45:33Z","title":"On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T18:52:46.048613Z"},"links":{"citing_paper":"/paper/2507.07016"},"observation_digest":"sha256:2553f677885668fc3d8b15453f604f7bd616e527192c84159da19a82d4094764","observation_id":"c1315242-0fd3-45f0-9453-3e551c0b5c65","resolution":{"observed_at":"2026-08-06T18:52:46.357101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T18:52:46.335817Z","title":"TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning","venue":null,"work_id":"e388aedb-834b-4d5e-94ad-ffeba4b6c728","year":null},"citing_paper":{"arxiv_id":"2507.07016","last_updated":"2025-07-09T16:45:33Z","snapshot_observed_at":"2026-08-06T18:46:59.459082Z","submitted_at":"2025-07-09T16:45:33Z","title":"On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T18:52:46.054053Z"},"links":{"citing_paper":"/paper/2507.07016"},"observation_digest":"sha256:9bc832ac5b729e92c1f52e21d41dd529af8df396c39c846ce27d2d877a734220","observation_id":"e760d433-20d5-448b-a653-90b6b9b40222","resolution":{"observed_at":"2026-08-06T18:52:46.341087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T18:52:46.317865Z","title":"Available: https://eigen.tuxfamily.org","venue":null,"work_id":"804220fb-fe32-4fb1-afe5-503a06261224","year":null},"citing_paper":{"arxiv_id":"2507.07016","last_updated":"2025-07-09T16:45:33Z","snapshot_observed_at":"2026-08-06T18:46:59.459082Z","submitted_at":"2025-07-09T16:45:33Z","title":"On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T18:52:46.058642Z"},"links":{"citing_paper":"/paper/2507.07016"},"observation_digest":"sha256:b0c1b0e01ea1096d607b573af558b913362a930ca065b3ef2093185145b80d29","observation_id":"e2124670-515f-45a1-8bdc-6a0ba59a08a6","resolution":{"observed_at":"2026-08-06T18:52:46.323672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.07016","last_updated":"2025-07-09T16:45:33Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T18:46:59.459082Z","submitted_at":"2025-07-09T16:45:33Z","title":"On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence"},"reference_resolution":{"displayed":6,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":4},"total_outbound_references":6},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2507.07016."}