{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UJDIC4LP73O75ODKXRLOJKA27X","short_pith_number":"pith:UJDIC4LP","schema_version":"1.0","canonical_sha256":"a24681716ffeddfeb86abc56e4a81afdf13421321f02ab7ee4a6f43ef7563273","source":{"kind":"arxiv","id":"2410.04880","version":1},"attestation_state":"computed","paper":{"title":"Improved detection of discarded fish species through BoxAL active learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aloysius van Helmond, Angelo Mencarelli, Arjan Vroegop, Gert Kootstra, Maria Sokolova, Pieter M. Blok","submitted_at":"2024-10-07T10:01:30Z","abstract_excerpt":"In recent years, powerful data-driven deep-learning techniques have been developed and applied for automated catch registration. However, these methods are dependent on the labelled data, which is time-consuming, labour-intensive, expensive to collect and need expert knowledge. In this study, we present an active learning technique, named BoxAL, which includes estimation of epistemic certainty of the Faster R-CNN object-detection model. The method allows selecting the most uncertain training images from an unlabeled pool, which are then used to train the object-detection model. To evaluate the"},"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":"2410.04880","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-07T10:01:30Z","cross_cats_sorted":[],"title_canon_sha256":"4f75f4aa4d76fec2805d94d0f07b7b18990349e3ecf5c458dbdf0acd6a51e1de","abstract_canon_sha256":"a8ec4663a3a9bb0f00b6fdcb9c7bd82babae15eaf736a00d685bde6107e42529"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:16:57.675743Z","signature_b64":"LdAqrh+f4aSlS9fc8pQv69cBC8NKAG9kNuV07pmTkl2p/WGLNjLvFyqqtefeoUfIZGU9wv7n/y/+OD5+GzS2AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a24681716ffeddfeb86abc56e4a81afdf13421321f02ab7ee4a6f43ef7563273","last_reissued_at":"2026-07-05T09:16:57.675239Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:16:57.675239Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improved detection of discarded fish species through BoxAL active learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aloysius van Helmond, Angelo Mencarelli, Arjan Vroegop, Gert Kootstra, Maria Sokolova, Pieter M. Blok","submitted_at":"2024-10-07T10:01:30Z","abstract_excerpt":"In recent years, powerful data-driven deep-learning techniques have been developed and applied for automated catch registration. However, these methods are dependent on the labelled data, which is time-consuming, labour-intensive, expensive to collect and need expert knowledge. In this study, we present an active learning technique, named BoxAL, which includes estimation of epistemic certainty of the Faster R-CNN object-detection model. The method allows selecting the most uncertain training images from an unlabeled pool, which are then used to train the object-detection model. To evaluate the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04880","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.04880/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":"2410.04880","created_at":"2026-07-05T09:16:57.675306+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.04880v1","created_at":"2026-07-05T09:16:57.675306+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04880","created_at":"2026-07-05T09:16:57.675306+00:00"},{"alias_kind":"pith_short_12","alias_value":"UJDIC4LP73O7","created_at":"2026-07-05T09:16:57.675306+00:00"},{"alias_kind":"pith_short_16","alias_value":"UJDIC4LP73O75ODK","created_at":"2026-07-05T09:16:57.675306+00:00"},{"alias_kind":"pith_short_8","alias_value":"UJDIC4LP","created_at":"2026-07-05T09:16:57.675306+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/UJDIC4LP73O75ODKXRLOJKA27X","json":"https://pith.science/pith/UJDIC4LP73O75ODKXRLOJKA27X.json","graph_json":"https://pith.science/api/pith-number/UJDIC4LP73O75ODKXRLOJKA27X/graph.json","events_json":"https://pith.science/api/pith-number/UJDIC4LP73O75ODKXRLOJKA27X/events.json","paper":"https://pith.science/paper/UJDIC4LP"},"agent_actions":{"view_html":"https://pith.science/pith/UJDIC4LP73O75ODKXRLOJKA27X","download_json":"https://pith.science/pith/UJDIC4LP73O75ODKXRLOJKA27X.json","view_paper":"https://pith.science/paper/UJDIC4LP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.04880&json=true","fetch_graph":"https://pith.science/api/pith-number/UJDIC4LP73O75ODKXRLOJKA27X/graph.json","fetch_events":"https://pith.science/api/pith-number/UJDIC4LP73O75ODKXRLOJKA27X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UJDIC4LP73O75ODKXRLOJKA27X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UJDIC4LP73O75ODKXRLOJKA27X/action/storage_attestation","attest_author":"https://pith.science/pith/UJDIC4LP73O75ODKXRLOJKA27X/action/author_attestation","sign_citation":"https://pith.science/pith/UJDIC4LP73O75ODKXRLOJKA27X/action/citation_signature","submit_replication":"https://pith.science/pith/UJDIC4LP73O75ODKXRLOJKA27X/action/replication_record"}},"created_at":"2026-07-05T09:16:57.675306+00:00","updated_at":"2026-07-05T09:16:57.675306+00:00"}