{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:AIMWATO4TNNFEPL32JYUS75N5H","short_pith_number":"pith:AIMWATO4","schema_version":"1.0","canonical_sha256":"0219604ddc9b5a523d7bd271497fade9c52e6d65676715cb36dcd2f327568c6d","source":{"kind":"arxiv","id":"1906.06628","version":1},"attestation_state":"computed","paper":{"title":"General classification of light curves using extreme boosting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.SR","authors_text":"Andrew Tkachenko, Chris Engelbrecht, Ian Whittingham, Refilwe Kgoadi","submitted_at":"2019-06-15T23:57:44Z","abstract_excerpt":"A significant degree of misclassification of variable stars through the application of machine learning methods to survey data motivates a search for more reliable and accurate machine learning procedures, especially in light of the very large data cubes that will be generated by future surveys and the need for immediate production of accurate, formalised catalogues of variable behaviour to enable science to proceed. In this study, the efficiency of an ensemble machine learning procedure utilising extreme boosting was determined by application to a large sample of data from the OGLE III and IV"},"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":"1906.06628","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2019-06-15T23:57:44Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"8e9913558b1c0fca1b95d80c4987ba41e5ca3a78f64e0aeeba109e7cc2bd514d","abstract_canon_sha256":"31ac730a36441293ecb2b90e54604bf83c7ea197cbd819d9a643d42875b3c46d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:13.137694Z","signature_b64":"ChwHDEVm3kXNWh34err2d8Nf1vo1bPon0vfzxJGtpxExSWzLGRIkmVtdLu+IgaYsiX34oOQy9CkaEfPgKEBgDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0219604ddc9b5a523d7bd271497fade9c52e6d65676715cb36dcd2f327568c6d","last_reissued_at":"2026-05-17T23:43:13.136740Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:13.136740Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"General classification of light curves using extreme boosting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.SR","authors_text":"Andrew Tkachenko, Chris Engelbrecht, Ian Whittingham, Refilwe Kgoadi","submitted_at":"2019-06-15T23:57:44Z","abstract_excerpt":"A significant degree of misclassification of variable stars through the application of machine learning methods to survey data motivates a search for more reliable and accurate machine learning procedures, especially in light of the very large data cubes that will be generated by future surveys and the need for immediate production of accurate, formalised catalogues of variable behaviour to enable science to proceed. In this study, the efficiency of an ensemble machine learning procedure utilising extreme boosting was determined by application to a large sample of data from the OGLE III and IV"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06628","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":"1906.06628","created_at":"2026-05-17T23:43:13.136904+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.06628v1","created_at":"2026-05-17T23:43:13.136904+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06628","created_at":"2026-05-17T23:43:13.136904+00:00"},{"alias_kind":"pith_short_12","alias_value":"AIMWATO4TNNF","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"AIMWATO4TNNFEPL3","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"AIMWATO4","created_at":"2026-05-18T12:33:12.712433+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/AIMWATO4TNNFEPL32JYUS75N5H","json":"https://pith.science/pith/AIMWATO4TNNFEPL32JYUS75N5H.json","graph_json":"https://pith.science/api/pith-number/AIMWATO4TNNFEPL32JYUS75N5H/graph.json","events_json":"https://pith.science/api/pith-number/AIMWATO4TNNFEPL32JYUS75N5H/events.json","paper":"https://pith.science/paper/AIMWATO4"},"agent_actions":{"view_html":"https://pith.science/pith/AIMWATO4TNNFEPL32JYUS75N5H","download_json":"https://pith.science/pith/AIMWATO4TNNFEPL32JYUS75N5H.json","view_paper":"https://pith.science/paper/AIMWATO4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.06628&json=true","fetch_graph":"https://pith.science/api/pith-number/AIMWATO4TNNFEPL32JYUS75N5H/graph.json","fetch_events":"https://pith.science/api/pith-number/AIMWATO4TNNFEPL32JYUS75N5H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AIMWATO4TNNFEPL32JYUS75N5H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AIMWATO4TNNFEPL32JYUS75N5H/action/storage_attestation","attest_author":"https://pith.science/pith/AIMWATO4TNNFEPL32JYUS75N5H/action/author_attestation","sign_citation":"https://pith.science/pith/AIMWATO4TNNFEPL32JYUS75N5H/action/citation_signature","submit_replication":"https://pith.science/pith/AIMWATO4TNNFEPL32JYUS75N5H/action/replication_record"}},"created_at":"2026-05-17T23:43:13.136904+00:00","updated_at":"2026-05-17T23:43:13.136904+00:00"}