{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OZ3DY6BJXYTL2GHEAB53NH6TVP","short_pith_number":"pith:OZ3DY6BJ","schema_version":"1.0","canonical_sha256":"76763c7829be26bd18e4007bb69fd3abca30df4f0c1b64e2d6f848c32bdf0cb4","source":{"kind":"arxiv","id":"1804.03765","version":3},"attestation_state":"computed","paper":{"title":"Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"A. Krone-Martins, A. Mahabal, A. Z. Vitorelli, B. Quint, E. E. O. Ishida, E. Gangler (for the COIN collaboration), J. M. Burgess, J. W. Barrett, N. Kennamer, R. Beck, R. S. de Souza, R. Vilalta, S. Gonzalez-Gaitan","submitted_at":"2018-04-11T00:55:25Z","abstract_excerpt":"We report a framework for spectroscopic follow-up design for optimizing supernova photometric classification. The strategy accounts for the unavoidable mismatch between spectroscopic and photometric samples, and can be used even in the beginning of a new survey -- without any initial training set. The framework falls under the umbrella of active learning (AL), a class of algorithms that aims to minimize labelling costs by identifying a few, carefully chosen, objects which have high potential in improving the classifier predictions. As a proof of concept, we use the simulated data released afte"},"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":"1804.03765","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2018-04-11T00:55:25Z","cross_cats_sorted":["astro-ph.CO"],"title_canon_sha256":"699340be3a3623398c4920106ee75afb5ff2bb760a6f2c9142184a90c12ec73e","abstract_canon_sha256":"468ead4b62ea2d9fafee24162aea6772e8bcd4ad6ca6e62dd9ca1b37e7c86fe0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:02.997033Z","signature_b64":"nMm+lRHzV87+mbam3l5wp2822DJNBrYbldj3JpCpWx2s65JnJ/SRR11mkt3CMK2DRDpu3ZfdpCdBBTQK9G8qCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76763c7829be26bd18e4007bb69fd3abca30df4f0c1b64e2d6f848c32bdf0cb4","last_reissued_at":"2026-05-17T23:57:02.996448Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:02.996448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"A. Krone-Martins, A. Mahabal, A. Z. Vitorelli, B. Quint, E. E. O. Ishida, E. Gangler (for the COIN collaboration), J. M. Burgess, J. W. Barrett, N. Kennamer, R. Beck, R. S. de Souza, R. Vilalta, S. Gonzalez-Gaitan","submitted_at":"2018-04-11T00:55:25Z","abstract_excerpt":"We report a framework for spectroscopic follow-up design for optimizing supernova photometric classification. The strategy accounts for the unavoidable mismatch between spectroscopic and photometric samples, and can be used even in the beginning of a new survey -- without any initial training set. The framework falls under the umbrella of active learning (AL), a class of algorithms that aims to minimize labelling costs by identifying a few, carefully chosen, objects which have high potential in improving the classifier predictions. As a proof of concept, we use the simulated data released afte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.03765","kind":"arxiv","version":3},"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":"1804.03765","created_at":"2026-05-17T23:57:02.996546+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.03765v3","created_at":"2026-05-17T23:57:02.996546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.03765","created_at":"2026-05-17T23:57:02.996546+00:00"},{"alias_kind":"pith_short_12","alias_value":"OZ3DY6BJXYTL","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OZ3DY6BJXYTL2GHE","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OZ3DY6BJ","created_at":"2026-05-18T12:32:43.782077+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/OZ3DY6BJXYTL2GHEAB53NH6TVP","json":"https://pith.science/pith/OZ3DY6BJXYTL2GHEAB53NH6TVP.json","graph_json":"https://pith.science/api/pith-number/OZ3DY6BJXYTL2GHEAB53NH6TVP/graph.json","events_json":"https://pith.science/api/pith-number/OZ3DY6BJXYTL2GHEAB53NH6TVP/events.json","paper":"https://pith.science/paper/OZ3DY6BJ"},"agent_actions":{"view_html":"https://pith.science/pith/OZ3DY6BJXYTL2GHEAB53NH6TVP","download_json":"https://pith.science/pith/OZ3DY6BJXYTL2GHEAB53NH6TVP.json","view_paper":"https://pith.science/paper/OZ3DY6BJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.03765&json=true","fetch_graph":"https://pith.science/api/pith-number/OZ3DY6BJXYTL2GHEAB53NH6TVP/graph.json","fetch_events":"https://pith.science/api/pith-number/OZ3DY6BJXYTL2GHEAB53NH6TVP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OZ3DY6BJXYTL2GHEAB53NH6TVP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OZ3DY6BJXYTL2GHEAB53NH6TVP/action/storage_attestation","attest_author":"https://pith.science/pith/OZ3DY6BJXYTL2GHEAB53NH6TVP/action/author_attestation","sign_citation":"https://pith.science/pith/OZ3DY6BJXYTL2GHEAB53NH6TVP/action/citation_signature","submit_replication":"https://pith.science/pith/OZ3DY6BJXYTL2GHEAB53NH6TVP/action/replication_record"}},"created_at":"2026-05-17T23:57:02.996546+00:00","updated_at":"2026-05-17T23:57:02.996546+00:00"}