{"as_of":"2026-08-16T08:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b17c0857d914206c88f912334a08a0976fc08ac0a09838f7e60cf05ba1889190","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":39,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:58:38.455307Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":115,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-12T17:58:43.302897Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2411.12104","last_updated":"2024-11-18T22:32:06Z","snapshot_observed_at":"2026-08-15T01:29:28.603429Z","submitted_at":"2024-11-18T22:32:06Z","title":"Is Locational Marginal Price All You Need for Locational Marginal Emission?","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T17:58:43.302897Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2411.12104"},"observation_digest":"sha256:b3a0dd1428956f4d2b4192da058c9d493e8bb823c15a44fac5d0cc515f868feb","observation_id":"f4d312b5-7646-47dc-ae67-29cdfe82ed16","resolution":{"observed_at":"2026-08-12T17:58:43.302897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-12T05:32:45.143606Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.00329","last_updated":"2025-01-17T12:53:37Z","snapshot_observed_at":"2026-08-12T10:22:04.004547Z","submitted_at":"2024-11-30T03:02:50Z","title":"Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T05:32:45.143606Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2412.00329"},"observation_digest":"sha256:28e9acb936411b760d795a374cc42fc0520e9e93140c4c81aa32423804b04ea6","observation_id":"ea50bf7c-7abf-431f-a643-5b6f60c489ca","resolution":{"observed_at":"2026-08-12T05:32:45.143606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-11T21:50:55.358507Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.04081","last_updated":"2024-12-05T11:32:14Z","snapshot_observed_at":"2026-08-15T05:00:52.221682Z","submitted_at":"2024-12-05T11:32:14Z","title":"Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T21:50:55.358507Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2412.04081"},"observation_digest":"sha256:934507a4342858781fc9f44db1fd5c03d5fa36726f7c1ffa6263f6fd552d9467","observation_id":"2d84a037-97ba-41e9-bd0c-d09d31868c59","resolution":{"observed_at":"2026-08-11T21:50:55.358507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-11T17:48:49.108853Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.08526","last_updated":"2024-12-11T16:37:44Z","snapshot_observed_at":"2026-08-13T09:46:12.207991Z","submitted_at":"2024-12-11T16:37:44Z","title":"Spend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T17:48:49.108853Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2412.08526"},"observation_digest":"sha256:aa8e75aea7eb32ee9d05a064e3b562b4664f4c550e4ce95fcbe20bfb7efe1418","observation_id":"a65da475-e332-44c6-a1ba-f048920849cf","resolution":{"observed_at":"2026-08-11T17:48:49.108853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-11T11:26:19.270434Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15511","last_updated":"2024-12-20T02:55:07Z","snapshot_observed_at":"2026-08-15T11:08:56.196218Z","submitted_at":"2024-12-20T02:55:07Z","title":"RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T11:26:19.270434Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2412.15511"},"observation_digest":"sha256:e2167a569a8295f8ba4169dc98dbb078b38b334e44f79b7e5f5ed295e2ddaec2","observation_id":"f1833bbf-65bb-41b6-bd0d-26e3ab758e2e","resolution":{"observed_at":"2026-08-11T11:26:19.270434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-11T05:35:44.712498Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.17376","last_updated":"2024-12-23T08:24:44Z","snapshot_observed_at":"2026-08-15T04:16:07.517036Z","submitted_at":"2024-12-23T08:24:44Z","title":"How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T05:35:44.712498Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2412.17376"},"observation_digest":"sha256:bfaea56aee675edf9b3a7cd5a2dedaa1ef33f24e89514e5e899987c9e9878a4f","observation_id":"d6b78dc8-ff1f-40c0-abcf-8628f65511dd","resolution":{"observed_at":"2026-08-11T05:35:44.712498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-10T16:28:52.123369Z","title":"Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.13181","last_updated":"2025-01-22T19:26:36Z","snapshot_observed_at":"2026-08-14T13:11:51.283186Z","submitted_at":"2025-01-22T19:26:36Z","title":"Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T16:28:52.123369Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2501.13181"},"observation_digest":"sha256:fe77f2b3b7d91e33539969bd9c57a55ccc7739c6db0f6e0031240100d83ef85b","observation_id":"d39be8a1-37ac-4f76-9df3-7f2da7c55323","resolution":{"observed_at":"2026-08-10T16:28:52.123369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-10T20:10:20.309203Z","title":"Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00046","last_updated":"2025-01-16T08:54:44Z","snapshot_observed_at":"2026-08-13T09:01:37.746502Z","submitted_at":"2025-01-16T08:54:44Z","title":"Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T20:10:20.309203Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2502.00046"},"observation_digest":"sha256:694486245937688abbd66e429e0589957637bf9eaf308019660b5c62aa4e8baf","observation_id":"e54d76d6-fe98-409a-9b5b-e31e68daa704","resolution":{"observed_at":"2026-08-10T20:10:20.309203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-09T04:46:25.395529Z","title":"IEEE Blockchain Ini- tiative, 7(2):90–99","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.03512","last_updated":"2025-02-10T02:34:39Z","snapshot_observed_at":"2026-08-14T10:44:31.001456Z","submitted_at":"2025-02-05T18:46:20Z","title":"YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing Multi-Objective Optimization based DPO for Text-to-Image Alignment","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-09T04:46:25.395529Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2502.03512"},"observation_digest":"sha256:24ee65cc85995c026166fde99ba1a1c4479b1fa49673f9ba1f2b2957cda58fff","observation_id":"3124d317-6717-424d-adbe-93d861779a51","resolution":{"observed_at":"2026-08-09T04:46:25.395529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-08T18:41:32.387695Z","title":"Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05610","last_updated":"2025-03-15T09:28:13Z","snapshot_observed_at":"2026-08-10T23:07:57.614834Z","submitted_at":"2025-02-08T15:34:52Z","title":"Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T18:41:32.387695Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2502.05610"},"observation_digest":"sha256:1dd67571f2caf327c6c0f37ac0e34e829819d3996834e2e3fe3e965dfff3596c","observation_id":"2cd3fa8d-fd05-46fa-8d26-4ba391a18cd5","resolution":{"observed_at":"2026-08-08T18:41:32.387695Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2503.10666","last_updated":"2026-04-27T14:34:31Z","snapshot_observed_at":"2026-08-08T17:42:06.220487Z","submitted_at":"2025-03-09T19:49:31Z","title":"Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-23T00:05:26.205947Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2503.10666"},"observation_digest":"sha256:c829e5b9e5c7827ff48fc7086b61400b9766afb361192d454a054a0d54b50a0d","observation_id":"162a961f-d281-4ffb-a66a-738a60c019b0","resolution":{"observed_at":"2026-05-23T00:07:17.268780Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-16T00:58:38.455307Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.02491","last_updated":"2026-07-17T18:59:34Z","snapshot_observed_at":"2026-08-16T00:47:15.239066Z","submitted_at":"2025-05-05T09:17:08Z","title":"Non-Markovianity and memory enhancement in Quantum Reservoir Computing","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T00:58:38.455307Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2505.02491"},"observation_digest":"sha256:bbac95d6198cfb7e55f9be0a4b80e7adac44ba0095bdda7bf1d9125bef19ba58","observation_id":"c9e10156-24c1-40af-900d-51faa0b2d8ae","resolution":{"observed_at":"2026-08-16T00:58:38.455307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-15T22:16:09.651674Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.07615","last_updated":"2025-07-16T17:59:28Z","snapshot_observed_at":"2026-08-15T22:09:49.479566Z","submitted_at":"2025-05-12T14:36:47Z","title":"Diffused Responsibility: Analyzing the Energy Consumption of Generative Text-to-Audio Diffusion Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T22:16:09.651674Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2505.07615"},"observation_digest":"sha256:0c36cbb8e8af1c96cea93367e31f7fbda661f098e728c88bf6bbb46457cf38c1","observation_id":"a0eb88be-afc9-458f-955e-4d2c5bf49262","resolution":{"observed_at":"2026-08-15T22:16:09.651674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-15T20:39:26.786114Z","title":"Carbontracker: Track- ing and predicting the carbon footprint of training deep learning models.arXiv preprint arXiv:2007.03051, 2020","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.12556","last_updated":"2025-05-18T22:05:11Z","snapshot_observed_at":"2026-08-15T20:29:08.237680Z","submitted_at":"2025-05-18T22:05:11Z","title":"Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T20:39:26.786114Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2505.12556"},"observation_digest":"sha256:4b3ad02f5b950b36ba587a836ef929fa3e8294e5e6b78b4eef492f16ee1c9d84","observation_id":"c43beb56-ce64-4602-8e2f-86b6665676cf","resolution":{"observed_at":"2026-08-15T20:39:26.786114Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-07T11:36:25.849010Z","title":"Wolff Anthony, Benjamin Kanding, and Raghaven- dra Selvan","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.01774","last_updated":"2025-06-03T08:44:31Z","snapshot_observed_at":"2026-08-09T22:52:26.590111Z","submitted_at":"2025-06-02T15:19:49Z","title":"Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:36:25.849010Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2506.01774"},"observation_digest":"sha256:69e006f5015b200a5ecf6f2c524b3b30dce23bf1da143fd01f62c05486907746","observation_id":"2982508b-91e6-4876-a6ea-44c65eedc84f","resolution":{"observed_at":"2026-08-07T11:36:25.849010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-07T04:47:07.311697Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.09683","last_updated":"2025-06-11T13:02:00Z","snapshot_observed_at":"2026-08-15T10:24:06.618055Z","submitted_at":"2025-06-11T13:02:00Z","title":"Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T04:47:07.311697Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2506.09683"},"observation_digest":"sha256:5e8e82d2f0f34880f059e3283b3be0049d25ccf1c59411d28a2b6170de7bbfe1","observation_id":"bbf4285b-be7a-44bb-8b69-b26ef55b331a","resolution":{"observed_at":"2026-08-07T04:47:07.311697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-06T20:49:02.026851Z","title":"Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.01803","last_updated":"2025-07-02T15:21:40Z","snapshot_observed_at":"2026-08-13T09:45:11.676371Z","submitted_at":"2025-07-02T15:21:40Z","title":"Towards Decentralized and Sustainable Foundation Model Training with the Edge","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:02.026851Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2507.01803"},"observation_digest":"sha256:1e51fac03e39ddc65726d827cf2eb5631b1abb2c8ade59ac7126fc7ec551fe13","observation_id":"00627497-0b08-4d0f-be99-26d9563cc3ab","resolution":{"observed_at":"2026-08-06T20:49:02.026851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-06T17:14:19.291983Z","title":"arXiv preprint arXiv:2007.03051 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.11417","last_updated":"2025-07-15T15:44:03Z","snapshot_observed_at":"2026-08-13T15:55:55.415974Z","submitted_at":"2025-07-15T15:44:03Z","title":"Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:14:19.291983Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2507.11417"},"observation_digest":"sha256:e68a98268d29f7e572e53cb330f6a9db41e437e0534532408035042bee78d365","observation_id":"46d8866b-546e-445f-b2ce-aa3684ee87a9","resolution":{"observed_at":"2026-08-06T17:14:19.291983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-15T18:03:46.760169Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19559","last_updated":"2025-07-25T08:26:53Z","snapshot_observed_at":"2026-08-15T17:59:21.155501Z","submitted_at":"2025-07-25T08:26:53Z","title":"Towards Sustainability Model Cards","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T18:03:46.760169Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2507.19559"},"observation_digest":"sha256:b4b81e4f7b5981c1dbf52309a635fc91ec1f4530115f0043e00fda5eee7e235c","observation_id":"b555bbab-1635-4e32-879c-637f512a678a","resolution":{"observed_at":"2026-08-15T18:03:46.760169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T21:58:20.986218Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.07691","last_updated":"2025-08-11T07:07:55Z","snapshot_observed_at":"2026-08-10T14:55:37.601150Z","submitted_at":"2025-08-11T07:07:55Z","title":"Energy and Quality of Surrogate-Assisted Search Algorithms: a First Analysis","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-05T21:58:20.986218Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2508.07691"},"observation_digest":"sha256:56e976828541003c987b323c35389aef35800f9321923ed82c82aa8f86bd642e","observation_id":"da894f6e-5cb3-4a71-ae82-64a53ba9a196","resolution":{"observed_at":"2026-08-05T21:58:20.986218Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T17:03:25.114160Z","title":"arXiv preprint arXiv:2007.03051 (2021)","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2509.00045","last_updated":"2025-09-03T05:15:22Z","snapshot_observed_at":"2026-08-13T22:08:01.718745Z","submitted_at":"2025-08-24T09:53:33Z","title":"Performance is not All You Need: Sustainability Considerations for Algorithms","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T17:03:25.114160Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2509.00045"},"observation_digest":"sha256:94f805aaee9d2f579e15767c27dbb968fa1e7cd147541528735f93d81ae8876b","observation_id":"a8eab23a-e5ad-4f9c-b52a-26203639530b","resolution":{"observed_at":"2026-08-05T17:03:25.114160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-04T17:57:53.510466Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2509.10367","last_updated":"2025-09-12T16:00:49Z","snapshot_observed_at":"2026-08-10T16:44:37.603187Z","submitted_at":"2025-09-12T16:00:49Z","title":"A Discrepancy-Based Perspective on Dataset Condensation","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-04T17:57:53.510466Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2509.10367"},"observation_digest":"sha256:98aff4897e1c28c53f15d9a394779683caecc7a441ec32efb4bff4aac917be6f","observation_id":"5827fe0a-d5b0-4024-9ee8-8d819b957d88","resolution":{"observed_at":"2026-08-04T17:57:53.510466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2509.24517","last_updated":"2026-08-13T09:23:53Z","snapshot_observed_at":"2026-08-16T08:08:57.647790Z","submitted_at":"2025-09-29T09:34:53Z","title":"Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-22T13:02:13.420496Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2509.24517"},"observation_digest":"sha256:e17d817744726c20a4ac42de396747d3e446cf8a904c7ac31b48d0d3cb55aa53","observation_id":"6a1da7ef-c593-49eb-8aa8-1a11e6d294ab","resolution":{"observed_at":"2026-05-22T13:04:52.559027Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2511.04776","last_updated":"2026-05-19T11:11:39Z","snapshot_observed_at":"2026-08-15T02:15:05.242652Z","submitted_at":"2025-11-06T19:52:02Z","title":"Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-18T00:33:28.444546Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2511.04776"},"observation_digest":"sha256:c826fbfd6f4b3df6d373783f19ceb62bf05ba8cb854b754de7bb9a4280dec69e","observation_id":"fd8ecf3f-ac87-4a03-94a4-21675fd88a2c","resolution":{"observed_at":"2026-05-18T00:35:32.524414Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2511.04776","last_updated":"2026-05-19T11:11:39Z","snapshot_observed_at":"2026-08-15T02:15:05.242652Z","submitted_at":"2025-11-06T19:52:02Z","title":"Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T18:54:54.484299Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2511.04776"},"observation_digest":"sha256:85d23dad94a89457c78904047b57731ce9a536181413947985ad18c7f2a3ff42","observation_id":"093e1e4e-eda4-4757-abc2-8fcb2d8bb9ea","resolution":{"observed_at":"2026-05-21T18:55:29.769193Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2511.17031","last_updated":"2026-05-12T19:37:16Z","snapshot_observed_at":"2026-08-15T14:59:38.870070Z","submitted_at":"2025-11-21T08:12:47Z","title":"Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-17T21:08:23.500137Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2511.17031"},"observation_digest":"sha256:c29ab20e7caa848e59f27e593bd5dfd552b380945ca2ee350d8c170eb2126b49","observation_id":"905a8d4b-a03c-41d1-a4e4-c78d5aec1513","resolution":{"observed_at":"2026-05-17T21:10:16.427683Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2604.09048","last_updated":"2026-04-10T07:15:58Z","snapshot_observed_at":"2026-08-16T07:48:19.185082Z","submitted_at":"2026-04-10T07:15:58Z","title":"Watt Counts: Energy-Aware Benchmark for Sustainable LLM Inference on Heterogeneous GPU Architectures","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T17:33:59.777818Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2604.09048"},"observation_digest":"sha256:c1e44241d80b2069d8cbcaade00ec57586e43152a7348ac3eba127d4d9d2df0b","observation_id":"5efaf128-a4f4-4928-bb6b-5ce7cc02e99b","resolution":{"observed_at":"2026-05-11T06:36:03.469662Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-07-12T23:32:59.467653Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2604.09054","last_updated":"2026-06-23T01:22:05Z","snapshot_observed_at":"2026-08-12T19:06:58.306086Z","submitted_at":"2026-04-10T07:27:55Z","title":"HAFM: Hierarchical Autoregressive Foundation Model for Music Accompaniment Generation","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-12T23:32:59.467653Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2604.09054"},"observation_digest":"sha256:88ee5964a5f685274e0f03c18cbfe78f3411b79da2184b934d2c5adf7c8498c7","observation_id":"3fc48dbf-f94b-4d68-8af8-65629ca34838","resolution":{"observed_at":"2026-07-12T23:32:59.467653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2604.19757","last_updated":"2026-03-23T09:47:55Z","snapshot_observed_at":"2026-08-04T11:06:46.959364Z","submitted_at":"2026-03-23T09:47:55Z","title":"Transparent Screening for LLM Inference and Training Impacts","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-15T01:00:32.131801Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2604.19757"},"observation_digest":"sha256:0112de33631862f243e8015563aa10e3453bf08eee0d1988bf7dd426845fbe68","observation_id":"ad2f62d0-e859-48e2-b291-5fedd0349eb8","resolution":{"observed_at":"2026-05-15T01:03:25.335544Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2605.01793","last_updated":"2026-05-03T09:17:56Z","snapshot_observed_at":"2026-08-16T01:28:02.452577Z","submitted_at":"2026-05-03T09:17:56Z","title":"Analytic Framework for Estimating Memory Cost","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-09T16:04:51.490820Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2605.01793"},"observation_digest":"sha256:a7eb23cfb803c8d3dc6aa2c5edb992c61e8ccf0c84a8c681160b6a5c442c62e3","observation_id":"c1360e7e-94a7-4199-aea4-9630e11cc37f","resolution":{"observed_at":"2026-05-11T16:36:08.294867Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2605.05416","last_updated":"2026-05-06T20:20:17Z","snapshot_observed_at":"2026-08-11T17:40:44.514893Z","submitted_at":"2026-05-06T20:20:17Z","title":"From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-08T15:43:50.422887Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2605.05416"},"observation_digest":"sha256:dee5716611050417ab544dd7440ee7aa7c93191ae09f4114736db66c01b0069c","observation_id":"0b7b2df9-d49f-4dab-95a4-69acc9eea716","resolution":{"observed_at":"2026-05-11T18:31:12.887791Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2605.22393","last_updated":"2026-05-21T12:26:47Z","snapshot_observed_at":"2026-08-08T09:30:29.893033Z","submitted_at":"2026-05-21T12:26:47Z","title":"Nf-PEAK: Process-Based Energy Attribution for Nextflow Workflows on Kubernetes Clusters","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-22T04:16:21.684291Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2605.22393"},"observation_digest":"sha256:c0217d2126aebc311c2b4c7b21e54aeb78fbaa7ff93f88505a6ca700944ac1b3","observation_id":"f0aed9a9-d615-4fde-9f85-985a25b912f2","resolution":{"observed_at":"2026-05-22T04:21:03.359209Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2605.24561","last_updated":"2026-05-23T12:56:29Z","snapshot_observed_at":"2026-08-02T21:35:48.849824Z","submitted_at":"2026-05-23T12:56:29Z","title":"CARINA: Carbon-Aware Execution of Recurrent Industrial Analytics","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-30T12:28:22.934242Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2605.24561"},"observation_digest":"sha256:6f19a8afdfd30281d776f5bedfe1092e05cf7e72640d2b5badd7fac8d21aeaf5","observation_id":"10b801a8-f7e6-4ff7-b23b-2374a205d7b0","resolution":{"observed_at":"2026-06-30T12:34:38.791982Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2606.07553","last_updated":"2026-05-23T13:29:45Z","snapshot_observed_at":"2026-08-10T02:41:37.022797Z","submitted_at":"2026-05-23T13:29:45Z","title":"MedicalRec: Medical recommender system for image classification without retraining","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-30T14:19:53.500581Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2606.07553"},"observation_digest":"sha256:4f580b2049bc0d25f98c8ffcec6afc2c41a757377894569ba764e351b0637454","observation_id":"d6c0a21a-3de3-4339-8242-572403001e75","resolution":{"observed_at":"2026-06-30T14:24:44.741383Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2606.08087","last_updated":"2026-06-06T10:23:18Z","snapshot_observed_at":"2026-08-08T01:15:20.773864Z","submitted_at":"2026-06-06T10:23:18Z","title":"Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T19:29:08.886649Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2606.08087"},"observation_digest":"sha256:105cb5a49e2fb3e3f270b28cca60692ededf06d5a3e68dbd4b04645e29e413a4","observation_id":"4f356880-115d-4658-8818-1c2c4be764ec","resolution":{"observed_at":"2026-07-02T21:47:28.113144Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":"2007.03051","doi":"10.48550/arxiv.2007.03051","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","venue":"arXiv (Cornell University)","work_id":"5266ac43-fd62-4de0-beb0-39a11e93a12f","year":2007},"citing_paper":{"arxiv_id":"2606.23047","last_updated":"2026-06-22T08:56:35Z","snapshot_observed_at":"2026-08-08T10:20:49.900322Z","submitted_at":"2026-06-22T08:56:35Z","title":"Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-06-26T06:41:04.713285Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2606.23047"},"observation_digest":"sha256:578490daf38f873a31ef4b4e981dfc4e5ccdc2723d3700f24ff6b7a12636d17f","observation_id":"c3786667-94e2-49be-998e-0bf426b6482e","resolution":{"observed_at":"2026-07-04T12:29:52.137169Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-14T23:38:08.151369+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-02T12:37:16.293093Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.22568","last_updated":"2026-05-31T18:29:16Z","snapshot_observed_at":"2026-08-14T18:25:30.194744Z","submitted_at":"2026-05-31T18:29:16Z","title":"Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-02T12:37:16.293093Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2607.22568"},"observation_digest":"sha256:3eb2d05679afec798bba49277e836aeebb4665da83dc63b27884bac67de217a4","observation_id":"178af1ac-c2a3-42f9-96c5-b38c5b027e9a","resolution":{"observed_at":"2026-08-02T12:37:16.293093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-12T00:52:45.244762Z","title":"arXiv preprint (2020) https://doi.org/10.48550/arXiv.2007.03051","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.09998","last_updated":"2026-08-07T13:52:38Z","snapshot_observed_at":"2026-08-14T23:09:55.330593Z","submitted_at":"2026-08-07T13:52:38Z","title":"Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T00:52:45.244762Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2608.09998"},"observation_digest":"sha256:60cc999202c05578a5e0296b5185fe5b3f366b4e3acb6f79c8e657b177488dd8","observation_id":"2eb12531-de93-4f94-9129-3de79bef579a","resolution":{"observed_at":"2026-08-12T00:52:45.244762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03051","snapshot_observed_at":"2026-08-12T00:53:32.117164Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.09999","last_updated":"2026-08-07T13:56:18Z","snapshot_observed_at":"2026-08-16T03:18:20.998921Z","submitted_at":"2026-08-07T13:56:18Z","title":"Robustness of transferability estimation metrics for medical imaging","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T00:53:32.117164Z"},"links":{"cited_paper":"/paper/2007.03051","citing_paper":"/paper/2608.09999"},"observation_digest":"sha256:96c36e046bcb19d3c9844be8ec34a87cf9743ca1ddde881201a7d11410159024","observation_id":"a1d077ea-a433-405c-8ca2-ac253b0248ed","resolution":{"observed_at":"2026-08-12T00:53:32.117164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2007.03051/citation-record","integrity":"/paper/2007.03051/integrity","json":"/paper/2007.03051/citation-record.json","paper":"/paper/2007.03051"},"outbound":[],"paper":{"arxiv_id":"2007.03051","last_updated":"2020-07-06T20:24:31Z","latest_version":1,"primary_category":"cs.CY","snapshot_observed_at":"2026-08-14T13:11:27.922295Z","submitted_at":"2020-07-06T20:24:31Z","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 39 inbound Pith citation observations for arXiv:2007.03051."}