{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:E5K2ATUKPUPMJ77PXXMHUYRK4D","short_pith_number":"pith:E5K2ATUK","schema_version":"1.0","canonical_sha256":"2755a04e8a7d1ec4ffefbdd87a622ae0e63b8ce26eb808d34b5f9d46aa3f6382","source":{"kind":"arxiv","id":"1601.01739","version":1},"attestation_state":"computed","paper":{"title":"Photometric Redshift Estimation for Quasars by Integration of KNN and SVM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Bo Han, Hongpeng Ding, Yanxia Zhang, Yongheng Zhao","submitted_at":"2016-01-08T01:20:11Z","abstract_excerpt":"The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem for a long time and exists in the current photometric redshift estimation approaches (such as $k$-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of $k$-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding"},"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":"1601.01739","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2016-01-08T01:20:11Z","cross_cats_sorted":[],"title_canon_sha256":"4d21b10e0b8e765809a3145ab82cf73562aa882001d9287c47b7567a1c09944c","abstract_canon_sha256":"fa27875e58a0e8384aaf37fbe36011efebc259f0d69324023d4c2ae2dec14033"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:13:56.538030Z","signature_b64":"XQP0QapswzBgF34ZbCWbuEeE14sOPHg4/ayE66XgbC3FfeD+dWcNE96YpLo6Hkzgf+r4IYIpXG48JPHNY63zAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2755a04e8a7d1ec4ffefbdd87a622ae0e63b8ce26eb808d34b5f9d46aa3f6382","last_reissued_at":"2026-05-18T01:13:56.537574Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:13:56.537574Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Photometric Redshift Estimation for Quasars by Integration of KNN and SVM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Bo Han, Hongpeng Ding, Yanxia Zhang, Yongheng Zhao","submitted_at":"2016-01-08T01:20:11Z","abstract_excerpt":"The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem for a long time and exists in the current photometric redshift estimation approaches (such as $k$-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of $k$-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.01739","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":"1601.01739","created_at":"2026-05-18T01:13:56.537635+00:00"},{"alias_kind":"arxiv_version","alias_value":"1601.01739v1","created_at":"2026-05-18T01:13:56.537635+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.01739","created_at":"2026-05-18T01:13:56.537635+00:00"},{"alias_kind":"pith_short_12","alias_value":"E5K2ATUKPUPM","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"E5K2ATUKPUPMJ77P","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"E5K2ATUK","created_at":"2026-05-18T12:30:12.583610+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/E5K2ATUKPUPMJ77PXXMHUYRK4D","json":"https://pith.science/pith/E5K2ATUKPUPMJ77PXXMHUYRK4D.json","graph_json":"https://pith.science/api/pith-number/E5K2ATUKPUPMJ77PXXMHUYRK4D/graph.json","events_json":"https://pith.science/api/pith-number/E5K2ATUKPUPMJ77PXXMHUYRK4D/events.json","paper":"https://pith.science/paper/E5K2ATUK"},"agent_actions":{"view_html":"https://pith.science/pith/E5K2ATUKPUPMJ77PXXMHUYRK4D","download_json":"https://pith.science/pith/E5K2ATUKPUPMJ77PXXMHUYRK4D.json","view_paper":"https://pith.science/paper/E5K2ATUK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1601.01739&json=true","fetch_graph":"https://pith.science/api/pith-number/E5K2ATUKPUPMJ77PXXMHUYRK4D/graph.json","fetch_events":"https://pith.science/api/pith-number/E5K2ATUKPUPMJ77PXXMHUYRK4D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E5K2ATUKPUPMJ77PXXMHUYRK4D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E5K2ATUKPUPMJ77PXXMHUYRK4D/action/storage_attestation","attest_author":"https://pith.science/pith/E5K2ATUKPUPMJ77PXXMHUYRK4D/action/author_attestation","sign_citation":"https://pith.science/pith/E5K2ATUKPUPMJ77PXXMHUYRK4D/action/citation_signature","submit_replication":"https://pith.science/pith/E5K2ATUKPUPMJ77PXXMHUYRK4D/action/replication_record"}},"created_at":"2026-05-18T01:13:56.537635+00:00","updated_at":"2026-05-18T01:13:56.537635+00:00"}