{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NCLAD5KEQTSSQCOEXTHOZO5ZSG","short_pith_number":"pith:NCLAD5KE","schema_version":"1.0","canonical_sha256":"689601f54484e52809c4bcceecbbb991b6f84acf80f9a49542d90dee3a735263","source":{"kind":"arxiv","id":"1905.03063","version":1},"attestation_state":"computed","paper":{"title":"Identification of Young Stellar Object candidates in the $Gaia$ DR2 x AllWISE catalogue with machine learning methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.GA","astro-ph.IM"],"primary_cat":"astro-ph.SR","authors_text":"\\'A. K\\'osp\\'al, Cs. Kiss, E. Szegedi-Elek, E. Varga-Vereb\\'elyi, G. Marton, J. Varga, L. Szabados, M. Kun, P. \\'Abrah\\'am, R. Beck, S. Hodgkin","submitted_at":"2019-05-08T13:23:14Z","abstract_excerpt":"The second $Gaia$ Data Release (DR2) contains astrometric and photometric data for more than 1.6 billion objects with mean $Gaia$ $G$ magnitude $<$20.7, including many Young Stellar Objects (YSOs) in different evolutionary stages. In order to explore the YSO population of the Milky Way, we combined the $Gaia$ DR2 database with WISE and Planck measurements and made an all-sky probabilistic catalogue of YSOs using machine learning techniques, such as Support Vector Machines, Random Forests, or Neural Networks. Our input catalogue contains 103 million objects from the DR2xAllWISE cross-match tabl"},"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":"1905.03063","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2019-05-08T13:23:14Z","cross_cats_sorted":["astro-ph.GA","astro-ph.IM"],"title_canon_sha256":"6c1f03708caf081466221be5a103fe0c56bcdfebed7f35f2f188d1150c354065","abstract_canon_sha256":"4a23b0516e002af5d89802e11167fa40e991e1c55ea22b44dc92dfa3bef5c753"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:26.535232Z","signature_b64":"PqKhuSM1MNThfRiDHKu1OUULbtIjQODQ8nB/qEOCL2OmB4rtgOkMpGQeXa7rHJ9pKsPxgzovGizqLrlC6D9xCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"689601f54484e52809c4bcceecbbb991b6f84acf80f9a49542d90dee3a735263","last_reissued_at":"2026-05-17T23:45:26.534759Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:26.534759Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identification of Young Stellar Object candidates in the $Gaia$ DR2 x AllWISE catalogue with machine learning methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.GA","astro-ph.IM"],"primary_cat":"astro-ph.SR","authors_text":"\\'A. K\\'osp\\'al, Cs. Kiss, E. Szegedi-Elek, E. Varga-Vereb\\'elyi, G. Marton, J. Varga, L. Szabados, M. Kun, P. \\'Abrah\\'am, R. Beck, S. Hodgkin","submitted_at":"2019-05-08T13:23:14Z","abstract_excerpt":"The second $Gaia$ Data Release (DR2) contains astrometric and photometric data for more than 1.6 billion objects with mean $Gaia$ $G$ magnitude $<$20.7, including many Young Stellar Objects (YSOs) in different evolutionary stages. In order to explore the YSO population of the Milky Way, we combined the $Gaia$ DR2 database with WISE and Planck measurements and made an all-sky probabilistic catalogue of YSOs using machine learning techniques, such as Support Vector Machines, Random Forests, or Neural Networks. Our input catalogue contains 103 million objects from the DR2xAllWISE cross-match tabl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03063","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":"1905.03063","created_at":"2026-05-17T23:45:26.534821+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.03063v1","created_at":"2026-05-17T23:45:26.534821+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03063","created_at":"2026-05-17T23:45:26.534821+00:00"},{"alias_kind":"pith_short_12","alias_value":"NCLAD5KEQTSS","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"NCLAD5KEQTSSQCOE","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"NCLAD5KE","created_at":"2026-05-18T12:33:24.271573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.19413","citing_title":"Gaia21bja: pre-main sequence star with quasi-periodic bursts","ref_index":180,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13189","citing_title":"The Accretion Process on Protostars","ref_index":241,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NCLAD5KEQTSSQCOEXTHOZO5ZSG","json":"https://pith.science/pith/NCLAD5KEQTSSQCOEXTHOZO5ZSG.json","graph_json":"https://pith.science/api/pith-number/NCLAD5KEQTSSQCOEXTHOZO5ZSG/graph.json","events_json":"https://pith.science/api/pith-number/NCLAD5KEQTSSQCOEXTHOZO5ZSG/events.json","paper":"https://pith.science/paper/NCLAD5KE"},"agent_actions":{"view_html":"https://pith.science/pith/NCLAD5KEQTSSQCOEXTHOZO5ZSG","download_json":"https://pith.science/pith/NCLAD5KEQTSSQCOEXTHOZO5ZSG.json","view_paper":"https://pith.science/paper/NCLAD5KE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.03063&json=true","fetch_graph":"https://pith.science/api/pith-number/NCLAD5KEQTSSQCOEXTHOZO5ZSG/graph.json","fetch_events":"https://pith.science/api/pith-number/NCLAD5KEQTSSQCOEXTHOZO5ZSG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NCLAD5KEQTSSQCOEXTHOZO5ZSG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NCLAD5KEQTSSQCOEXTHOZO5ZSG/action/storage_attestation","attest_author":"https://pith.science/pith/NCLAD5KEQTSSQCOEXTHOZO5ZSG/action/author_attestation","sign_citation":"https://pith.science/pith/NCLAD5KEQTSSQCOEXTHOZO5ZSG/action/citation_signature","submit_replication":"https://pith.science/pith/NCLAD5KEQTSSQCOEXTHOZO5ZSG/action/replication_record"}},"created_at":"2026-05-17T23:45:26.534821+00:00","updated_at":"2026-05-17T23:45:26.534821+00:00"}