{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NRNRE5GHJFE5TUPHAUFBESCWMN","short_pith_number":"pith:NRNRE5GH","schema_version":"1.0","canonical_sha256":"6c5b1274c74949d9d1e7050a12485663600abcb81b64843b46864aa44956f571","source":{"kind":"arxiv","id":"1801.03305","version":1},"attestation_state":"computed","paper":{"title":"Probabilistic performance estimators for computational chemistry methods: the empirical cumulative distribution function of absolute errors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"physics.chem-ph","authors_text":"Andreas Savin, Pascal Pernot","submitted_at":"2018-01-10T10:53:43Z","abstract_excerpt":"Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulativ"},"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":"1801.03305","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2018-01-10T10:53:43Z","cross_cats_sorted":["physics.data-an"],"title_canon_sha256":"d8e2072d6d25d90d447251dd6e446a637870f175810ca28c563953f3a806bfe3","abstract_canon_sha256":"35efef21936b7fa699129433932fa113d6c7a28fd90475a7047cb38ff6e14767"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:51.793708Z","signature_b64":"bcM3YVFjg9x9xzdBF7FYkanAwfhrp8LfQMh5ggmXNkkIbS/4jGdi54cxgVS+D/cjaqLSMtEPio3lw6PwUeFQBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c5b1274c74949d9d1e7050a12485663600abcb81b64843b46864aa44956f571","last_reissued_at":"2026-05-18T00:20:51.793184Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:51.793184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic performance estimators for computational chemistry methods: the empirical cumulative distribution function of absolute errors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"physics.chem-ph","authors_text":"Andreas Savin, Pascal Pernot","submitted_at":"2018-01-10T10:53:43Z","abstract_excerpt":"Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulativ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.03305","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":""},"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":"1801.03305","created_at":"2026-05-18T00:20:51.793264+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.03305v1","created_at":"2026-05-18T00:20:51.793264+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.03305","created_at":"2026-05-18T00:20:51.793264+00:00"},{"alias_kind":"pith_short_12","alias_value":"NRNRE5GHJFE5","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NRNRE5GHJFE5TUPH","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NRNRE5GH","created_at":"2026-05-18T12:32:40.477152+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/NRNRE5GHJFE5TUPHAUFBESCWMN","json":"https://pith.science/pith/NRNRE5GHJFE5TUPHAUFBESCWMN.json","graph_json":"https://pith.science/api/pith-number/NRNRE5GHJFE5TUPHAUFBESCWMN/graph.json","events_json":"https://pith.science/api/pith-number/NRNRE5GHJFE5TUPHAUFBESCWMN/events.json","paper":"https://pith.science/paper/NRNRE5GH"},"agent_actions":{"view_html":"https://pith.science/pith/NRNRE5GHJFE5TUPHAUFBESCWMN","download_json":"https://pith.science/pith/NRNRE5GHJFE5TUPHAUFBESCWMN.json","view_paper":"https://pith.science/paper/NRNRE5GH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.03305&json=true","fetch_graph":"https://pith.science/api/pith-number/NRNRE5GHJFE5TUPHAUFBESCWMN/graph.json","fetch_events":"https://pith.science/api/pith-number/NRNRE5GHJFE5TUPHAUFBESCWMN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NRNRE5GHJFE5TUPHAUFBESCWMN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NRNRE5GHJFE5TUPHAUFBESCWMN/action/storage_attestation","attest_author":"https://pith.science/pith/NRNRE5GHJFE5TUPHAUFBESCWMN/action/author_attestation","sign_citation":"https://pith.science/pith/NRNRE5GHJFE5TUPHAUFBESCWMN/action/citation_signature","submit_replication":"https://pith.science/pith/NRNRE5GHJFE5TUPHAUFBESCWMN/action/replication_record"}},"created_at":"2026-05-18T00:20:51.793264+00:00","updated_at":"2026-05-18T00:20:51.793264+00:00"}