{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DUEXERRMPGIF2UHTXVKBZ2WBUW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"abc37dd47cc640718d8c02cbf90a0f438f0dc9a54780d819b8bf00a10e692fdf","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-21T17:53:27Z","title_canon_sha256":"f5c0b70879c1b11d59b70504d202da6f4439579d0463942b8a8112b20ed56eec"},"schema_version":"1.0","source":{"id":"2411.14354","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.14354","created_at":"2026-07-05T09:38:45Z"},{"alias_kind":"arxiv_version","alias_value":"2411.14354v1","created_at":"2026-07-05T09:38:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14354","created_at":"2026-07-05T09:38:45Z"},{"alias_kind":"pith_short_12","alias_value":"DUEXERRMPGIF","created_at":"2026-07-05T09:38:45Z"},{"alias_kind":"pith_short_16","alias_value":"DUEXERRMPGIF2UHT","created_at":"2026-07-05T09:38:45Z"},{"alias_kind":"pith_short_8","alias_value":"DUEXERRM","created_at":"2026-07-05T09:38:45Z"}],"graph_snapshots":[{"event_id":"sha256:6147978d7c27f77895cde0656dd9f1f6233da49bed90b834e7b249b3a9b81720","target":"graph","created_at":"2026-07-05T09:38:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.14354/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While advances in machine learning with satellite imagery (SatML) are facilitating environmental monitoring at a global scale, developing SatML models that are accurate and useful for local regions remains critical to understanding and acting on an ever-changing planet. As increasing attention and resources are being devoted to training SatML models with global data, it is important to understand when improvements in global models will make it easier to train or fine-tune models that are accurate in specific regions. To explore this question, we contrast local and global training paradigms for","authors_text":"Andrew Davies, Esther Rolf, Lucia Gordon, Milind Tambe","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-21T17:53:27Z","title":"Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14354","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6cef2cb00cc8ee85274282ff3d77e606b32488b359ea0cb39e11c0a60598e407","target":"record","created_at":"2026-07-05T09:38:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"abc37dd47cc640718d8c02cbf90a0f438f0dc9a54780d819b8bf00a10e692fdf","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-21T17:53:27Z","title_canon_sha256":"f5c0b70879c1b11d59b70504d202da6f4439579d0463942b8a8112b20ed56eec"},"schema_version":"1.0","source":{"id":"2411.14354","kind":"arxiv","version":1}},"canonical_sha256":"1d0972462c79905d50f3bd541ceac1a59ecea90083324ae2b85f2edd482666c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1d0972462c79905d50f3bd541ceac1a59ecea90083324ae2b85f2edd482666c2","first_computed_at":"2026-07-05T09:38:45.637481Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:38:45.637481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GJGmgZgm5+ZazG6laOS+D7VvbLGXVvsFseTSr5Vc5BCIp3p1IrTkjs9aqrsSs2S3QVb01muJSVHS1jKAw3ydAw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:38:45.638017Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.14354","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6cef2cb00cc8ee85274282ff3d77e606b32488b359ea0cb39e11c0a60598e407","sha256:6147978d7c27f77895cde0656dd9f1f6233da49bed90b834e7b249b3a9b81720"],"state_sha256":"1a392b8e09e6119dfc40cf25c27ff595977d36e4b770a17abd158ce47ac8e49f"}