{"as_of":"2026-08-08T17:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b2c591e1967c3667d9d46260c8f4e411f5cff8e17c69f7bddca29b51134a7d91","coverage":[{"denominator":11,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:56:20.548691Z","state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"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/2507.08832/citation-record","integrity":"/paper/2507.08832/integrity","json":"/paper/2507.08832/citation-record.json","paper":"/paper/2507.08832"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:56:20.637372Z","title":null,"venue":null,"work_id":"52387d8f-5d7a-4312-a33a-469f9b3eefd5","year":2001},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.523855Z"},"links":{"citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:523b89db7eb5183efd77d97eb77c964a9c5a74c1e33f4fd7fd31f6a21d5392ca","observation_id":"5adc91ea-5cc3-4e98-b3bf-8eaf03748459","resolution":{"observed_at":"2026-08-06T19:56:20.639790Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:56:20.631194Z","title":"Mahendra Dev","venue":null,"work_id":"f4372cf8-613e-4405-ba56-ce1a97064083","year":2012},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.527005Z"},"links":{"citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:ff34159f842eb9038979253c2f2fc06962917104b08adb78a4e01dcd013efe2a","observation_id":"4268066a-97b2-42cc-9058-1ff5e32caeaa","resolution":{"observed_at":"2026-08-06T19:56:20.633683Z","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":"2026-08-06T19:56:20.625358Z","title":null,"venue":null,"work_id":"1a35e30c-0762-4029-a3ef-9c60f75240d7","year":2019},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.529874Z"},"links":{"citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:00236749ed8c3ad9992adb8a06bd654087c82bbd7e85e1cca8f84814c86460c7","observation_id":"30d7f9f2-7a41-4f6f-9467-82d7fc4b698b","resolution":{"observed_at":"2026-08-06T19:56:20.627347Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.05492","last_updated":"2017-10-30T20:52:14Z","snapshot_observed_at":"2026-07-06T05:15:00.158639Z","submitted_at":"2016-10-18T09:11:51Z","title":"Federated Learning: Strategies for Improving Communication Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.05492","snapshot_observed_at":"2026-08-06T19:56:20.532881Z","title":"Brendan McMahan, Felix X","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.532881Z"},"links":{"cited_paper":"/paper/1610.05492","citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:0c680ca843b5dd168b47b866948478d6d3224398916571dbd0f7e57b0de5a2c8","observation_id":"9adf203c-db32-43e7-9539-646332546331","resolution":{"observed_at":"2026-08-06T19:56:20.532881Z","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":"2026-08-06T19:56:20.618810Z","title":null,"venue":null,"work_id":"a8151b55-2f43-404c-a1b3-81043eef3c43","year":2021},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.535800Z"},"links":{"citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:45df6c4f71d6d4c004149535a44b5472146d962fb9dccdd56a1413661f75f5d4","observation_id":"d3a4b5a7-f6e6-4183-a116-cf7ad8fc4964","resolution":{"observed_at":"2026-08-06T19:56:20.620835Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:56:20.612072Z","title":null,"venue":null,"work_id":"6d79b5e1-985d-4deb-ab7f-a2bebf94be4c","year":2014},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.538140Z"},"links":{"citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:d8c454106cad3853da4831001b46e8b6da2daff8835f0c86431fd96dfa36c53d","observation_id":"7a784688-504e-4537-b893-e2350ad42584","resolution":{"observed_at":"2026-08-06T19:56:20.614402Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13516","last_updated":"2023-05-22T22:09:41Z","snapshot_observed_at":"2026-08-02T15:37:26.541116Z","submitted_at":"2023-05-22T22:09:41Z","title":"Scaling Speech Technology to 1,000+ Languages","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13516","snapshot_observed_at":"2026-08-06T19:56:20.540912Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.540912Z"},"links":{"cited_paper":"/paper/2305.13516","citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:0e6d2ca2a8bcacd6ef3d2078c46809b85d27ee0d08272a639a1d81faf9af29b1","observation_id":"ab8d4adc-37bd-45eb-920c-f7370d130374","resolution":{"observed_at":"2026-08-06T19:56:20.540912Z","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":"2026-08-06T19:56:20.603545Z","title":"Pudumalar, P","venue":null,"work_id":"bb57f5c9-5391-4fcd-85ff-af5e62d2e21c","year":2016},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.542904Z"},"links":{"citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:699f2aff2a81689a3efe2c37d8e205b2e91eaf50d27deb31a89fa3d71ffef2ec","observation_id":"a2411d12-38bd-49dd-94de-26b5d07b4a9e","resolution":{"observed_at":"2026-08-06T19:56:20.606057Z","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":"2026-08-06T19:56:20.595572Z","title":null,"venue":null,"work_id":"4cff1c33-818e-4cf5-93b6-59e93e5f5c6a","year":2023},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.544733Z"},"links":{"citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:0ee9271a6f5ccc8ad9c0df3687c05a2d3bf862e5f17ff9b1c7f00f72810c3679","observation_id":"c76ea26e-7483-4223-aa19-1ccd2b77fda9","resolution":{"observed_at":"2026-08-06T19:56:20.597511Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:56:20.588754Z","title":null,"venue":null,"work_id":"e4af2100-14ce-4238-a9bd-1eacd8194b16","year":2009},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.546477Z"},"links":{"citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:da80fa45b4885284c625132e7cf999ccb8d4668968b4d41609c9db131470b539","observation_id":"7847b391-911c-41d3-86a5-62b446535f8b","resolution":{"observed_at":"2026-08-06T19:56:20.591584Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:56:20.580189Z","title":null,"venue":null,"work_id":"96d95881-b15f-4de9-9753-c4a5b638b3df","year":2020},"citing_paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:56:20.548691Z"},"links":{"citing_paper":"/paper/2507.08832"},"observation_digest":"sha256:c064535c8c591857c563e043f2ba49e7fe40efadb675ae0ed05ebdab5803c4d9","observation_id":"8d274041-76ae-43a2-901f-5454b8952ab0","resolution":{"observed_at":"2026-08-06T19:56:20.583700Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.08832","last_updated":"2025-07-06T06:18:41Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T19:49:54.521474Z","submitted_at":"2025-07-06T06:18:41Z","title":"A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting"},"reference_resolution":{"displayed":11,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":11},"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 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2507.08832."}