{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IRCMZIB6LMIW6MB3NXXXP6LJPU","short_pith_number":"pith:IRCMZIB6","schema_version":"1.0","canonical_sha256":"4444cca03e5b116f303b6def77f9697d26282be79a4cde4a604d4cafa1c8b69f","source":{"kind":"arxiv","id":"2504.17492","version":1},"attestation_state":"computed","paper":{"title":"Prototype-enhanced prediction in graph neural networks for climate applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Elena Fillola, Jeffrey Nicholas Clark, Matthew Rigby, Nawid Keshtmand, Raul Santos-Rodriguez","submitted_at":"2025-04-24T12:34:23Z","abstract_excerpt":"Data-driven emulators are increasingly being used to learn and emulate physics-based simulations, reducing computational expense and run time. Here, we present a structured way to improve the quality of these high-dimensional emulated outputs, through the use of prototypes: an approximation of the emulator's output passed as an input, which informs the model and leads to better predictions. We demonstrate our approach to emulate atmospheric dispersion, key for greenhouse gas emissions monitoring, by comparing a baseline model to models trained using prototypes as an additional input. The proto"},"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":"2504.17492","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-24T12:34:23Z","cross_cats_sorted":[],"title_canon_sha256":"a5f36b829d3ea52151e0ebdb393450a1321d81ea220e57a00d543822a4b7ea62","abstract_canon_sha256":"177e5129191a6ba99d88abc819e896e2b3b69cd453eb85a3a7f5f0567106cb0d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:29.736790Z","signature_b64":"h3MEuwmCg7IxMfvsd9lvf0WnCm2MDBdi3bW24ODhNbcqODe3Z5GqYp/onzqoOesA96bftOitxOY6dPLu9xOTCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4444cca03e5b116f303b6def77f9697d26282be79a4cde4a604d4cafa1c8b69f","last_reissued_at":"2026-07-05T10:53:29.736293Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:29.736293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prototype-enhanced prediction in graph neural networks for climate applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Elena Fillola, Jeffrey Nicholas Clark, Matthew Rigby, Nawid Keshtmand, Raul Santos-Rodriguez","submitted_at":"2025-04-24T12:34:23Z","abstract_excerpt":"Data-driven emulators are increasingly being used to learn and emulate physics-based simulations, reducing computational expense and run time. Here, we present a structured way to improve the quality of these high-dimensional emulated outputs, through the use of prototypes: an approximation of the emulator's output passed as an input, which informs the model and leads to better predictions. We demonstrate our approach to emulate atmospheric dispersion, key for greenhouse gas emissions monitoring, by comparing a baseline model to models trained using prototypes as an additional input. The proto"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17492","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/2504.17492/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":"2504.17492","created_at":"2026-07-05T10:53:29.736369+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.17492v1","created_at":"2026-07-05T10:53:29.736369+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17492","created_at":"2026-07-05T10:53:29.736369+00:00"},{"alias_kind":"pith_short_12","alias_value":"IRCMZIB6LMIW","created_at":"2026-07-05T10:53:29.736369+00:00"},{"alias_kind":"pith_short_16","alias_value":"IRCMZIB6LMIW6MB3","created_at":"2026-07-05T10:53:29.736369+00:00"},{"alias_kind":"pith_short_8","alias_value":"IRCMZIB6","created_at":"2026-07-05T10:53:29.736369+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IRCMZIB6LMIW6MB3NXXXP6LJPU","json":"https://pith.science/pith/IRCMZIB6LMIW6MB3NXXXP6LJPU.json","graph_json":"https://pith.science/api/pith-number/IRCMZIB6LMIW6MB3NXXXP6LJPU/graph.json","events_json":"https://pith.science/api/pith-number/IRCMZIB6LMIW6MB3NXXXP6LJPU/events.json","paper":"https://pith.science/paper/IRCMZIB6"},"agent_actions":{"view_html":"https://pith.science/pith/IRCMZIB6LMIW6MB3NXXXP6LJPU","download_json":"https://pith.science/pith/IRCMZIB6LMIW6MB3NXXXP6LJPU.json","view_paper":"https://pith.science/paper/IRCMZIB6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.17492&json=true","fetch_graph":"https://pith.science/api/pith-number/IRCMZIB6LMIW6MB3NXXXP6LJPU/graph.json","fetch_events":"https://pith.science/api/pith-number/IRCMZIB6LMIW6MB3NXXXP6LJPU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IRCMZIB6LMIW6MB3NXXXP6LJPU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IRCMZIB6LMIW6MB3NXXXP6LJPU/action/storage_attestation","attest_author":"https://pith.science/pith/IRCMZIB6LMIW6MB3NXXXP6LJPU/action/author_attestation","sign_citation":"https://pith.science/pith/IRCMZIB6LMIW6MB3NXXXP6LJPU/action/citation_signature","submit_replication":"https://pith.science/pith/IRCMZIB6LMIW6MB3NXXXP6LJPU/action/replication_record"}},"created_at":"2026-07-05T10:53:29.736369+00:00","updated_at":"2026-07-05T10:53:29.736369+00:00"}