{"as_of":"2026-08-08T21:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6622eac72aaeb1382d8cae388fdb29adaab77f0d75163a1a1d00860d0e0ef91b","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T13:53:40.637640Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"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/2507.20008/citation-record","integrity":"/paper/2507.20008/integrity","json":"/paper/2507.20008/citation-record.json","paper":"/paper/2507.20008"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1511.00000","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:53:41.506851Z","title":"Autoencoders,","venue":null,"work_id":"6e737c51-9e48-44b5-a12d-dbc3b755cf82","year":2015},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:39.118784Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:aa3e5db3d99f76744841a61537739c1c06fd7c29679bdab3627f1889f7c41683","observation_id":"f52a27e5-8345-426e-8dc0-ad027c1a9450","resolution":{"observed_at":"2026-08-06T13:53:41.576785Z","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":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-07-06T05:10:16.862707Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-06T13:53:39.210063Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:39.210063Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:31b74925f88a2608b598b93b3599f9af31d707b4172d13912315d9b6771673db","observation_id":"4ff50879-6d6f-4ea8-84f2-8e283893e9bb","resolution":{"observed_at":"2026-08-06T13:53:39.210063Z","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":"2026-08-06T13:53:43.067753Z","title":"Graph attention networks,","venue":null,"work_id":"1e9da519-8a54-4e25-be8b-ed3d66180b12","year":2018},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:39.304429Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:c93a56a8d6ee0bf9f6cedce97d9363e21cb2fc54ad32e28291af9f9273a51fb8","observation_id":"6d7a2c87-fef9-41e0-bdcc-63e11cc1bf10","resolution":{"observed_at":"2026-08-06T13:53:43.174109Z","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":{"arxiv_id":"1810.11953","last_updated":"2019-10-28T14:56:15Z","snapshot_observed_at":"2026-07-06T07:11:06.440653Z","submitted_at":"2018-10-29T04:50:18Z","title":"Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.11953","snapshot_observed_at":"2026-08-06T13:53:39.385558Z","title":"Failing loudly: An empirical study of methods for detecting dataset shift,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:39.385558Z"},"links":{"cited_paper":"/paper/1810.11953","citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:a06597defafa4ab970af86ea697be864019a245edaa88d65cedcabb319162d3e","observation_id":"c43decbd-4121-4b1b-8a9f-6beee40c8316","resolution":{"observed_at":"2026-08-06T13:53:39.385558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.01234","last_updated":"2023-01-01T23:42:32Z","snapshot_observed_at":"2026-08-05T15:59:57.906944Z","submitted_at":"2023-01-01T23:42:32Z","title":"AmbieGen: A Search-based Framework for Autonomous Systems Testing","version":1},"cited_work":{"arxiv_id":"2301.01234","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.01234","snapshot_observed_at":"2026-08-06T13:53:41.244560Z","title":"AmbieGen: A Search-based Framework for Autonomous Systems Testing","venue":"cs.RO","work_id":"3280d43e-fe3c-4d31-9a5c-ba12ab97cf1c","year":2023},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:39.531526Z"},"links":{"cited_paper":"/paper/2301.01234","citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:699b8dbb027b9185c598d4df5071584c9a29199c4e47c31a3be2b9f05d5c4d89","observation_id":"601bab71-b771-4280-af86-2d1a3dd7228a","resolution":{"observed_at":"2026-08-06T13:53:41.327629Z","resolver_source":"local_arxiv","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":{"arxiv_id":"2304.01982","last_updated":"2024-04-08T18:31:32Z","snapshot_observed_at":"2026-08-07T22:47:34.048228Z","submitted_at":"2023-04-04T17:37:06Z","title":"Rethinking the Role of Token Retrieval in Multi-Vector Retrieval","version":3},"cited_work":{"arxiv_id":"2304.01982","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.01982","snapshot_observed_at":"2026-08-06T13:53:40.952284Z","title":"Rethinking the Role of Token Retrieval in Multi-Vector Retrieval","venue":"cs.CL","work_id":"3db807ac-6d6e-4086-9d05-a17235644f11","year":2023},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:39.668791Z"},"links":{"cited_paper":"/paper/2304.01982","citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:48e437152186f6871fae01cdc5feef63d3fb4e93ce7568da3dc90c7486893e98","observation_id":"4d4a9666-75b6-462b-96ac-e372e1be385f","resolution":{"observed_at":"2026-08-06T13:53:41.124859Z","resolver_source":"local_arxiv","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":"2026-08-06T13:53:42.895060Z","title":"On analysis of gan-based image-to-image translation with gaussian noise injection,","venue":null,"work_id":"c3b8a332-82dd-4999-b511-ed89d8340647","year":2024},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:39.785132Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:56a8e473f4c45d30336b60b27c54609a7b9394bf944e3a42c92dea1593ea8631","observation_id":"1596296e-5705-401e-9449-51c1e41b8828","resolution":{"observed_at":"2026-08-06T13:53:42.988119Z","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":{"arxiv_id":"2403.01987","last_updated":"2024-03-04T12:31:32Z","snapshot_observed_at":"2026-07-06T17:39:07.926765Z","submitted_at":"2024-03-04T12:31:32Z","title":"Current-driven dynamics of antiferromagnetic skyrmions: from skyrmion Hall effects to hybrid inter-skyrmion scattering","version":1},"cited_work":{"arxiv_id":"2403.01987","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.01987","snapshot_observed_at":"2026-08-06T13:53:40.801192Z","title":"Current-driven dynamics of antiferromagnetic skyrmions: from skyrmion Hall effects to hybrid inter-skyrmion scattering","venue":"cond-mat.mtrl-sci","work_id":"58e210e3-2c0e-4185-b159-06698ae1d97f","year":2024},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:39.899765Z"},"links":{"cited_paper":"/paper/2403.01987","citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:2e03d14a94743ed0efcecd30d2dfb911faeef4a70176a0cda3743e6773d7c08f","observation_id":"60ad0db3-6930-40ec-a497-4dba163f71cc","resolution":{"observed_at":"2026-08-06T13:53:40.853637Z","resolver_source":"local_arxiv","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":{"arxiv_id":"2401.12345","last_updated":"2025-06-17T20:37:32Z","snapshot_observed_at":"2026-08-06T10:45:08.715526Z","submitted_at":"2024-01-22T20:20:48Z","title":"Distributionally Robust Receive Combining","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12345","snapshot_observed_at":"2026-08-06T13:53:39.982383Z","title":"Navig: Natural language-guided analysis with vision for image geo-localization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:39.982383Z"},"links":{"cited_paper":"/paper/2401.12345","citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:dee27ea4557334309eb79e8e61e277572b2d4524b42256f07826b1dbe1acccc0","observation_id":"72d6e4d9-aafb-4ce8-aad3-bf146e958a17","resolution":{"observed_at":"2026-08-06T13:53:39.982383Z","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":"2026-08-06T13:53:42.657730Z","title":"Xgboost: A scalable tree boosting system,","venue":null,"work_id":"b831ba29-11b2-4384-8029-90df6087dda2","year":2016},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:40.074522Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:8944ee01f563def4ab9a9113518a46cb451dcf8938c1812a728a679760c45a38","observation_id":"25114691-5993-4e50-a7d6-768c298e814d","resolution":{"observed_at":"2026-08-06T13:53:42.765032Z","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":"2026-08-06T13:53:42.541338Z","title":"Catboost: Unbiased boosting with categorical features,","venue":null,"work_id":"102331cc-bec1-48a6-abf4-5de3833ef791","year":2018},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:40.144938Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:9e690e20bb6623df1ed01a6027fe4d20b9c07676fbfdbb54df284d8e9979bb2d","observation_id":"8fdad2b8-39b2-4c2c-b604-8d3ca262e705","resolution":{"observed_at":"2026-08-06T13:53:42.605709Z","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":"2026-08-06T13:53:42.377630Z","title":"Lightgbm: A highly efficient gradient boosting decision tree,","venue":null,"work_id":"b2c9afd0-9800-453a-ad93-2d4a4780bebc","year":2017},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:40.255539Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:f299f6d9ace537d206406e60adecefb5be2f97e575f024354341b02050118221","observation_id":"81a69f61-2a0a-4ca3-a210-047c2fd191df","resolution":{"observed_at":"2026-08-06T13:53:42.469577Z","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":"2026-08-06T13:53:42.180436Z","title":"Timesnet: Temporal and inter-sample correlation exploration for multivariate time series forecasting,","venue":null,"work_id":"088d5037-9a4c-43d4-b440-ece16a1579ec","year":2023},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:40.372687Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:ff5847e5fa87801da9f431009cfc545d0c81d0b8854188504159cf72de68b257","observation_id":"c5d96d62-c81b-4cc9-9933-a17dfd894a0f","resolution":{"observed_at":"2026-08-06T13:53:42.298551Z","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":"2026-08-06T13:53:42.022417Z","title":"Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting,","venue":null,"work_id":"53869605-5a2f-4c95-b618-bc8d9bc208f4","year":2022},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:40.471805Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:07c85ddad28de1b92d3e01469cda2e3facd64912b331262799d803456b64b05d","observation_id":"e23f9ede-335f-4c90-913f-afca4886c889","resolution":{"observed_at":"2026-08-06T13:53:42.097205Z","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":"2026-08-06T13:53:41.828429Z","title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting,","venue":null,"work_id":"50a88d5f-fcaf-482e-b779-cec00da29ae3","year":2023},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:40.550440Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:f35f4b2a83af53671c40ad97724962aa8845ae50a85086cf40146a31add04cb5","observation_id":"619e6eec-a871-43bd-a085-f5c99f6304d3","resolution":{"observed_at":"2026-08-06T13:53:41.945488Z","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":"2026-08-06T13:53:41.659543Z","title":"Informer: Beyond efficient trans- former for long sequence time-series forecasting,","venue":null,"work_id":"36427e9e-a008-4ded-92bc-24aedc2586f3","year":2021},"citing_paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T13:53:40.637640Z"},"links":{"citing_paper":"/paper/2507.20008"},"observation_digest":"sha256:e031c36ef35e3f2757353a9e4c2003ccfffc89f8bf59c72873694ae2beb43268","observation_id":"889d4a77-334d-4ed2-8fa4-7b460b9027e1","resolution":{"observed_at":"2026-08-06T13:53:41.744317Z","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"}}],"paper":{"arxiv_id":"2507.20008","last_updated":"2025-07-26T16:55:16Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T05:03:56.999543Z","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":9},"total_outbound_references":16},"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 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2507.20008."}