{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LBJLO5J3KS3QD3JLRV32VNIEIF","short_pith_number":"pith:LBJLO5J3","schema_version":"1.0","canonical_sha256":"5852b7753b54b701ed2b8d77aab504416d955a0a7f3f6b514feeac7deb6ee4c4","source":{"kind":"arxiv","id":"2407.21231","version":1},"attestation_state":"computed","paper":{"title":"Towards an Integrated Performance Framework for Fire Science and Management Workflows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.LG","authors_text":"D. Crawl, H. Ahmed, I. Altintas, I. Perez, R. Shende, S. Purawat","submitted_at":"2024-07-30T22:37:25Z","abstract_excerpt":"Reliable performance metrics are necessary prerequisites to building large-scale end-to-end integrated workflows for collaborative scientific research, particularly within context of use-inspired decision making platforms with many concurrent users and when computing real-time and urgent results using large data. This work is a building block for the National Data Platform, which leverages multiple use-cases including the WIFIRE Data and Model Commons for wildfire behavior modeling and the EarthScope Consortium for collaborative geophysical research. This paper presents an artificial intellige"},"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":"2407.21231","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-30T22:37:25Z","cross_cats_sorted":["cs.PF"],"title_canon_sha256":"fed0322ed28ad6d30629d5e1ca81e07ea70f07a83f711f5711d0b68b9d0cc949","abstract_canon_sha256":"b74b4f780c11b0d805383fe4be185005285967de5b48f863f41b39ff790894af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:50:36.294104Z","signature_b64":"FZI0LxZmPTaQfzt9QiQyPYi16Vdt6sTagwFKKgD+/iTW48UA3StQBxcaruGnrHCmHS1QfKiCMI+iJhHLXfhkBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5852b7753b54b701ed2b8d77aab504416d955a0a7f3f6b514feeac7deb6ee4c4","last_reissued_at":"2026-07-05T08:50:36.293629Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:50:36.293629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards an Integrated Performance Framework for Fire Science and Management Workflows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.LG","authors_text":"D. Crawl, H. Ahmed, I. Altintas, I. Perez, R. Shende, S. Purawat","submitted_at":"2024-07-30T22:37:25Z","abstract_excerpt":"Reliable performance metrics are necessary prerequisites to building large-scale end-to-end integrated workflows for collaborative scientific research, particularly within context of use-inspired decision making platforms with many concurrent users and when computing real-time and urgent results using large data. This work is a building block for the National Data Platform, which leverages multiple use-cases including the WIFIRE Data and Model Commons for wildfire behavior modeling and the EarthScope Consortium for collaborative geophysical research. This paper presents an artificial intellige"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.21231","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/2407.21231/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":"2407.21231","created_at":"2026-07-05T08:50:36.293688+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.21231v1","created_at":"2026-07-05T08:50:36.293688+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.21231","created_at":"2026-07-05T08:50:36.293688+00:00"},{"alias_kind":"pith_short_12","alias_value":"LBJLO5J3KS3Q","created_at":"2026-07-05T08:50:36.293688+00:00"},{"alias_kind":"pith_short_16","alias_value":"LBJLO5J3KS3QD3JL","created_at":"2026-07-05T08:50:36.293688+00:00"},{"alias_kind":"pith_short_8","alias_value":"LBJLO5J3","created_at":"2026-07-05T08:50:36.293688+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.13730","citing_title":"BanditWare: A Contextual Bandit-based Framework for Hardware Prediction","ref_index":2024,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LBJLO5J3KS3QD3JLRV32VNIEIF","json":"https://pith.science/pith/LBJLO5J3KS3QD3JLRV32VNIEIF.json","graph_json":"https://pith.science/api/pith-number/LBJLO5J3KS3QD3JLRV32VNIEIF/graph.json","events_json":"https://pith.science/api/pith-number/LBJLO5J3KS3QD3JLRV32VNIEIF/events.json","paper":"https://pith.science/paper/LBJLO5J3"},"agent_actions":{"view_html":"https://pith.science/pith/LBJLO5J3KS3QD3JLRV32VNIEIF","download_json":"https://pith.science/pith/LBJLO5J3KS3QD3JLRV32VNIEIF.json","view_paper":"https://pith.science/paper/LBJLO5J3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.21231&json=true","fetch_graph":"https://pith.science/api/pith-number/LBJLO5J3KS3QD3JLRV32VNIEIF/graph.json","fetch_events":"https://pith.science/api/pith-number/LBJLO5J3KS3QD3JLRV32VNIEIF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LBJLO5J3KS3QD3JLRV32VNIEIF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LBJLO5J3KS3QD3JLRV32VNIEIF/action/storage_attestation","attest_author":"https://pith.science/pith/LBJLO5J3KS3QD3JLRV32VNIEIF/action/author_attestation","sign_citation":"https://pith.science/pith/LBJLO5J3KS3QD3JLRV32VNIEIF/action/citation_signature","submit_replication":"https://pith.science/pith/LBJLO5J3KS3QD3JLRV32VNIEIF/action/replication_record"}},"created_at":"2026-07-05T08:50:36.293688+00:00","updated_at":"2026-07-05T08:50:36.293688+00:00"}