{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:EERSEO7RDR5AOG3J6USZXA7QSY","short_pith_number":"pith:EERSEO7R","schema_version":"1.0","canonical_sha256":"2123223bf11c7a071b69f5259b83f0961749b8cb4c3fd1f71d9a75830edefad5","source":{"kind":"arxiv","id":"1909.05303","version":2},"attestation_state":"computed","paper":{"title":"A Hybrid Ensemble method for Pulsar Candidate Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Jianhua Zheng, Lei Qian, Mingtao Li, Yuanchao Wang, Zhichen Pan","submitted_at":"2019-09-11T18:47:43Z","abstract_excerpt":"In this paper, three ensemble methods: Random Forest, XGBoost, and a Hybrid Ensemble method were implemented to classify imbalanced pulsar candidates. To assist these methods, tree models were used to select features among 30 features of pulsar candidates from references. The skewness of the integrated pulse profile, chi-squared value for sine-squared fit to amended profile and best S/N value play important roles in Random Forest, while the skewness of the integrated pulse profile is one of the most significant features in XGBoost. More than 20 features were selected by their relative scores a"},"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":"1909.05303","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2019-09-11T18:47:43Z","cross_cats_sorted":[],"title_canon_sha256":"6c516d89346dfc27545be7721ff4a9a857a9e107025731089eec0cb9dfe5f999","abstract_canon_sha256":"bb4ecf2c479b61aaf2b01d63e7768d1d1d325039dcb9bcff2655e6b1dba49145"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:06:11.037135Z","signature_b64":"Lj76rGBGzsVF5jpccxc+aQJI3Xo7jR2Svb/1/qG/oY3ycui/w+B8v1NHyrxk9oPi2+2hf7IZrGYuopM7y/BPBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2123223bf11c7a071b69f5259b83f0961749b8cb4c3fd1f71d9a75830edefad5","last_reissued_at":"2026-07-05T00:06:11.036699Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:06:11.036699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Hybrid Ensemble method for Pulsar Candidate Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Jianhua Zheng, Lei Qian, Mingtao Li, Yuanchao Wang, Zhichen Pan","submitted_at":"2019-09-11T18:47:43Z","abstract_excerpt":"In this paper, three ensemble methods: Random Forest, XGBoost, and a Hybrid Ensemble method were implemented to classify imbalanced pulsar candidates. To assist these methods, tree models were used to select features among 30 features of pulsar candidates from references. The skewness of the integrated pulse profile, chi-squared value for sine-squared fit to amended profile and best S/N value play important roles in Random Forest, while the skewness of the integrated pulse profile is one of the most significant features in XGBoost. More than 20 features were selected by their relative scores a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.05303","kind":"arxiv","version":2},"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/1909.05303/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":"1909.05303","created_at":"2026-07-05T00:06:11.036762+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.05303v2","created_at":"2026-07-05T00:06:11.036762+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.05303","created_at":"2026-07-05T00:06:11.036762+00:00"},{"alias_kind":"pith_short_12","alias_value":"EERSEO7RDR5A","created_at":"2026-07-05T00:06:11.036762+00:00"},{"alias_kind":"pith_short_16","alias_value":"EERSEO7RDR5AOG3J","created_at":"2026-07-05T00:06:11.036762+00:00"},{"alias_kind":"pith_short_8","alias_value":"EERSEO7R","created_at":"2026-07-05T00:06:11.036762+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/EERSEO7RDR5AOG3J6USZXA7QSY","json":"https://pith.science/pith/EERSEO7RDR5AOG3J6USZXA7QSY.json","graph_json":"https://pith.science/api/pith-number/EERSEO7RDR5AOG3J6USZXA7QSY/graph.json","events_json":"https://pith.science/api/pith-number/EERSEO7RDR5AOG3J6USZXA7QSY/events.json","paper":"https://pith.science/paper/EERSEO7R"},"agent_actions":{"view_html":"https://pith.science/pith/EERSEO7RDR5AOG3J6USZXA7QSY","download_json":"https://pith.science/pith/EERSEO7RDR5AOG3J6USZXA7QSY.json","view_paper":"https://pith.science/paper/EERSEO7R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.05303&json=true","fetch_graph":"https://pith.science/api/pith-number/EERSEO7RDR5AOG3J6USZXA7QSY/graph.json","fetch_events":"https://pith.science/api/pith-number/EERSEO7RDR5AOG3J6USZXA7QSY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EERSEO7RDR5AOG3J6USZXA7QSY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EERSEO7RDR5AOG3J6USZXA7QSY/action/storage_attestation","attest_author":"https://pith.science/pith/EERSEO7RDR5AOG3J6USZXA7QSY/action/author_attestation","sign_citation":"https://pith.science/pith/EERSEO7RDR5AOG3J6USZXA7QSY/action/citation_signature","submit_replication":"https://pith.science/pith/EERSEO7RDR5AOG3J6USZXA7QSY/action/replication_record"}},"created_at":"2026-07-05T00:06:11.036762+00:00","updated_at":"2026-07-05T00:06:11.036762+00:00"}