{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QRE7RQ3REXVJOE2FHSXP4LRFZQ","short_pith_number":"pith:QRE7RQ3R","schema_version":"1.0","canonical_sha256":"8449f8c37125ea9713453caefe2e25cc1163cedb1267332aa4c47a04929d42b5","source":{"kind":"arxiv","id":"1805.05540","version":1},"attestation_state":"computed","paper":{"title":"Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"C. S. Ong, C. Wolf, H. Andernach, J. K. Banfield, L. Rudnick, M. J. Alger, O. I. Wong, R. P. Norris, S. S. Shabala","submitted_at":"2018-05-15T03:01:00Z","abstract_excerpt":"We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-area radio surveys like the Evolutionary Map of the Universe (EMU). Automated cross-identification will be critical for these future surveys, and machine learning may provide the tools to develop such methods. We apply a standard approach from computer vision to cross-identification, introducing one possible way of automating this problem, and explore the pros and cons of this approach. We apply our method to the 1.4 GH"},"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":"1805.05540","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2018-05-15T03:01:00Z","cross_cats_sorted":[],"title_canon_sha256":"996b94ef75380212242b18698d8aaed707fb7febf6f34e17b0f2c1cd8093bcf7","abstract_canon_sha256":"9a9e68c7ee3ed7841e232563d3e3d5d55c45cc3f0212d64ea3308635d57e8fc3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:46.269122Z","signature_b64":"Knk+08/z2FLY5xAKWZ3miitt34sTdvsJ6nq24gd3n7jAYBtfcYAoDfM0XcZ/LfODGGnOtf04nI+/kQ6O3At0Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8449f8c37125ea9713453caefe2e25cc1163cedb1267332aa4c47a04929d42b5","last_reissued_at":"2026-05-18T00:15:46.268589Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:46.268589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"C. S. Ong, C. Wolf, H. Andernach, J. K. Banfield, L. Rudnick, M. J. Alger, O. I. Wong, R. P. Norris, S. S. Shabala","submitted_at":"2018-05-15T03:01:00Z","abstract_excerpt":"We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-area radio surveys like the Evolutionary Map of the Universe (EMU). Automated cross-identification will be critical for these future surveys, and machine learning may provide the tools to develop such methods. We apply a standard approach from computer vision to cross-identification, introducing one possible way of automating this problem, and explore the pros and cons of this approach. We apply our method to the 1.4 GH"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.05540","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":"1805.05540","created_at":"2026-05-18T00:15:46.268681+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.05540v1","created_at":"2026-05-18T00:15:46.268681+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.05540","created_at":"2026-05-18T00:15:46.268681+00:00"},{"alias_kind":"pith_short_12","alias_value":"QRE7RQ3REXVJ","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QRE7RQ3REXVJOE2F","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QRE7RQ3R","created_at":"2026-05-18T12:32:46.962924+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/QRE7RQ3REXVJOE2FHSXP4LRFZQ","json":"https://pith.science/pith/QRE7RQ3REXVJOE2FHSXP4LRFZQ.json","graph_json":"https://pith.science/api/pith-number/QRE7RQ3REXVJOE2FHSXP4LRFZQ/graph.json","events_json":"https://pith.science/api/pith-number/QRE7RQ3REXVJOE2FHSXP4LRFZQ/events.json","paper":"https://pith.science/paper/QRE7RQ3R"},"agent_actions":{"view_html":"https://pith.science/pith/QRE7RQ3REXVJOE2FHSXP4LRFZQ","download_json":"https://pith.science/pith/QRE7RQ3REXVJOE2FHSXP4LRFZQ.json","view_paper":"https://pith.science/paper/QRE7RQ3R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.05540&json=true","fetch_graph":"https://pith.science/api/pith-number/QRE7RQ3REXVJOE2FHSXP4LRFZQ/graph.json","fetch_events":"https://pith.science/api/pith-number/QRE7RQ3REXVJOE2FHSXP4LRFZQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QRE7RQ3REXVJOE2FHSXP4LRFZQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QRE7RQ3REXVJOE2FHSXP4LRFZQ/action/storage_attestation","attest_author":"https://pith.science/pith/QRE7RQ3REXVJOE2FHSXP4LRFZQ/action/author_attestation","sign_citation":"https://pith.science/pith/QRE7RQ3REXVJOE2FHSXP4LRFZQ/action/citation_signature","submit_replication":"https://pith.science/pith/QRE7RQ3REXVJOE2FHSXP4LRFZQ/action/replication_record"}},"created_at":"2026-05-18T00:15:46.268681+00:00","updated_at":"2026-05-18T00:15:46.268681+00:00"}