{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:X74P3F73OVXAPJVJYRWJSQFABK","short_pith_number":"pith:X74P3F73","schema_version":"1.0","canonical_sha256":"bff8fd97fb756e07a6a9c46c9940a00a9ce971c708cfad9bd4445c4337b94ce7","source":{"kind":"arxiv","id":"2305.15149","version":1},"attestation_state":"computed","paper":{"title":"Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jana Kierdorf, Ribana Roscher","submitted_at":"2023-05-24T13:48:36Z","abstract_excerpt":"Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important. However, accurately determining harvest-readiness can be challenging due to the cauliflower head being covered by its canopy. While deep learning enables automated harvest-readiness estimation, errors can occur due to field-variability and limited training data. In this paper, we analyze the reliability of a harvest-readiness classifier with interpretable machine learning. By identifying clusters of saliency maps, we derive reliability scores for each classification re"},"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":"2305.15149","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-24T13:48:36Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c5ccdd3588a513d9a0ce8e187e63351b797fed2582efe9389a2980f32ad07b64","abstract_canon_sha256":"d0626b9d58ab674db8acecd2e3697fd3696f6cb74e3cf2da33c858cd6599e1fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:38:58.130159Z","signature_b64":"DuztoYsxGK/W2uPjJFfT+wJHCCUKE8AGES/DMxthDWfDHFy6soWveh/r9646S1jpVjPjjdxlibG1lOHutpUFAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bff8fd97fb756e07a6a9c46c9940a00a9ce971c708cfad9bd4445c4337b94ce7","last_reissued_at":"2026-07-05T06:38:58.129725Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:38:58.129725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jana Kierdorf, Ribana Roscher","submitted_at":"2023-05-24T13:48:36Z","abstract_excerpt":"Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important. However, accurately determining harvest-readiness can be challenging due to the cauliflower head being covered by its canopy. While deep learning enables automated harvest-readiness estimation, errors can occur due to field-variability and limited training data. In this paper, we analyze the reliability of a harvest-readiness classifier with interpretable machine learning. By identifying clusters of saliency maps, we derive reliability scores for each classification re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15149","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/2305.15149/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":"2305.15149","created_at":"2026-07-05T06:38:58.129781+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.15149v1","created_at":"2026-07-05T06:38:58.129781+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15149","created_at":"2026-07-05T06:38:58.129781+00:00"},{"alias_kind":"pith_short_12","alias_value":"X74P3F73OVXA","created_at":"2026-07-05T06:38:58.129781+00:00"},{"alias_kind":"pith_short_16","alias_value":"X74P3F73OVXAPJVJ","created_at":"2026-07-05T06:38:58.129781+00:00"},{"alias_kind":"pith_short_8","alias_value":"X74P3F73","created_at":"2026-07-05T06:38:58.129781+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/X74P3F73OVXAPJVJYRWJSQFABK","json":"https://pith.science/pith/X74P3F73OVXAPJVJYRWJSQFABK.json","graph_json":"https://pith.science/api/pith-number/X74P3F73OVXAPJVJYRWJSQFABK/graph.json","events_json":"https://pith.science/api/pith-number/X74P3F73OVXAPJVJYRWJSQFABK/events.json","paper":"https://pith.science/paper/X74P3F73"},"agent_actions":{"view_html":"https://pith.science/pith/X74P3F73OVXAPJVJYRWJSQFABK","download_json":"https://pith.science/pith/X74P3F73OVXAPJVJYRWJSQFABK.json","view_paper":"https://pith.science/paper/X74P3F73","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.15149&json=true","fetch_graph":"https://pith.science/api/pith-number/X74P3F73OVXAPJVJYRWJSQFABK/graph.json","fetch_events":"https://pith.science/api/pith-number/X74P3F73OVXAPJVJYRWJSQFABK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X74P3F73OVXAPJVJYRWJSQFABK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X74P3F73OVXAPJVJYRWJSQFABK/action/storage_attestation","attest_author":"https://pith.science/pith/X74P3F73OVXAPJVJYRWJSQFABK/action/author_attestation","sign_citation":"https://pith.science/pith/X74P3F73OVXAPJVJYRWJSQFABK/action/citation_signature","submit_replication":"https://pith.science/pith/X74P3F73OVXAPJVJYRWJSQFABK/action/replication_record"}},"created_at":"2026-07-05T06:38:58.129781+00:00","updated_at":"2026-07-05T06:38:58.129781+00:00"}