{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5C6MFVOOAE6UGPCF24QN4QYLIM","short_pith_number":"pith:5C6MFVOO","schema_version":"1.0","canonical_sha256":"e8bcc2d5ce013d433c45d720de430b430d979e73bb8fb766e0e9c041d1df9050","source":{"kind":"arxiv","id":"2403.18438","version":1},"attestation_state":"computed","paper":{"title":"Global Vegetation Modeling with Pre-Trained Weather Transformers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andreas Hotho, Anna Krause, Florian Gallusser, Pascal Janetzky, Simon Hentschel","submitted_at":"2024-03-27T10:45:16Z","abstract_excerpt":"Accurate vegetation models can produce further insights into the complex interaction between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for medium-range weather forecasting, we adapt the publicly available pre-trained FourCastNet to model vegetation activity while accounting for the short-term dynamics of climate variability. We investigate how the learned global representation"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2403.18438","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-27T10:45:16Z","cross_cats_sorted":[],"title_canon_sha256":"f2528ea9d5d7020852e5c1e83ee38c93750d11756a9e6111bd837f1f8183a4af","abstract_canon_sha256":"c341d90ed4e28e477f51e4919901a8d382541aa3a8c79eccf62df549c7a15eb1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:01:22.684524Z","signature_b64":"EZlBQcU3n13KpDWhFO/l0LG6aFxmJ+GGbENC/sbWxFbuDdg5DYAI60B536lDZXMt94yALuX8Q5D2o3TnJKv4Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8bcc2d5ce013d433c45d720de430b430d979e73bb8fb766e0e9c041d1df9050","last_reissued_at":"2026-07-05T08:01:22.684085Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:01:22.684085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Global Vegetation Modeling with Pre-Trained Weather Transformers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andreas Hotho, Anna Krause, Florian Gallusser, Pascal Janetzky, Simon Hentschel","submitted_at":"2024-03-27T10:45:16Z","abstract_excerpt":"Accurate vegetation models can produce further insights into the complex interaction between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for medium-range weather forecasting, we adapt the publicly available pre-trained FourCastNet to model vegetation activity while accounting for the short-term dynamics of climate variability. We investigate how the learned global representation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.18438","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.18438/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2403.18438","created_at":"2026-07-05T08:01:22.684142+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.18438v1","created_at":"2026-07-05T08:01:22.684142+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.18438","created_at":"2026-07-05T08:01:22.684142+00:00"},{"alias_kind":"pith_short_12","alias_value":"5C6MFVOOAE6U","created_at":"2026-07-05T08:01:22.684142+00:00"},{"alias_kind":"pith_short_16","alias_value":"5C6MFVOOAE6UGPCF","created_at":"2026-07-05T08:01:22.684142+00:00"},{"alias_kind":"pith_short_8","alias_value":"5C6MFVOO","created_at":"2026-07-05T08:01:22.684142+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27277","citing_title":"EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5C6MFVOOAE6UGPCF24QN4QYLIM","json":"https://pith.science/pith/5C6MFVOOAE6UGPCF24QN4QYLIM.json","graph_json":"https://pith.science/api/pith-number/5C6MFVOOAE6UGPCF24QN4QYLIM/graph.json","events_json":"https://pith.science/api/pith-number/5C6MFVOOAE6UGPCF24QN4QYLIM/events.json","paper":"https://pith.science/paper/5C6MFVOO"},"agent_actions":{"view_html":"https://pith.science/pith/5C6MFVOOAE6UGPCF24QN4QYLIM","download_json":"https://pith.science/pith/5C6MFVOOAE6UGPCF24QN4QYLIM.json","view_paper":"https://pith.science/paper/5C6MFVOO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.18438&json=true","fetch_graph":"https://pith.science/api/pith-number/5C6MFVOOAE6UGPCF24QN4QYLIM/graph.json","fetch_events":"https://pith.science/api/pith-number/5C6MFVOOAE6UGPCF24QN4QYLIM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5C6MFVOOAE6UGPCF24QN4QYLIM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5C6MFVOOAE6UGPCF24QN4QYLIM/action/storage_attestation","attest_author":"https://pith.science/pith/5C6MFVOOAE6UGPCF24QN4QYLIM/action/author_attestation","sign_citation":"https://pith.science/pith/5C6MFVOOAE6UGPCF24QN4QYLIM/action/citation_signature","submit_replication":"https://pith.science/pith/5C6MFVOOAE6UGPCF24QN4QYLIM/action/replication_record"}},"created_at":"2026-07-05T08:01:22.684142+00:00","updated_at":"2026-07-05T08:01:22.684142+00:00"}