{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:YB6N55O4QIR2P2TRBZSKYVQZR3","short_pith_number":"pith:YB6N55O4","schema_version":"1.0","canonical_sha256":"c07cdef5dc8223a7ea710e64ac56198ecf8a5b4bacec56ba3696c886181d8ded","source":{"kind":"arxiv","id":"1708.01986","version":2},"attestation_state":"computed","paper":{"title":"Identifying 3 moss species by deep learning, using the \"chopped picture\" method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"stat.ML","authors_text":"Mari Minagawa, Masanori Onishi, Takeshi Ise","submitted_at":"2017-08-07T04:37:23Z","abstract_excerpt":"In general, object identification tends not to work well on ambiguous, amorphous objects such as vegetation. In this study, we developed a simple but effective approach to identify ambiguous objects and applied the method to several moss species. As a result, the model correctly classified test images with accuracy more than 90%. Using this approach will help progress in computer vision studies."},"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":"1708.01986","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-08-07T04:37:23Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a0ab3d99b819cfa1b6d07e7be9bf7f08b286135e833265b9d6c8b7cdaf18efd4","abstract_canon_sha256":"c0d37e19d260a438324b6214a571ce8cd5244a390578e3ecc78b1fb0fb88e659"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:25.058432Z","signature_b64":"9uRFZI9SCO3Ih0so34CwtN2M6hvKwhgfXNKUXXFJ+lRMN6B5A/ujinm3Nc8zFaiRt01RL/BqZd4wHkl5AU4KAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c07cdef5dc8223a7ea710e64ac56198ecf8a5b4bacec56ba3696c886181d8ded","last_reissued_at":"2026-05-18T00:38:25.057513Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:25.057513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identifying 3 moss species by deep learning, using the \"chopped picture\" method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"stat.ML","authors_text":"Mari Minagawa, Masanori Onishi, Takeshi Ise","submitted_at":"2017-08-07T04:37:23Z","abstract_excerpt":"In general, object identification tends not to work well on ambiguous, amorphous objects such as vegetation. In this study, we developed a simple but effective approach to identify ambiguous objects and applied the method to several moss species. As a result, the model correctly classified test images with accuracy more than 90%. Using this approach will help progress in computer vision studies."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.01986","kind":"arxiv","version":2},"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":"1708.01986","created_at":"2026-05-18T00:38:25.057670+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.01986v2","created_at":"2026-05-18T00:38:25.057670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.01986","created_at":"2026-05-18T00:38:25.057670+00:00"},{"alias_kind":"pith_short_12","alias_value":"YB6N55O4QIR2","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"YB6N55O4QIR2P2TR","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"YB6N55O4","created_at":"2026-05-18T12:31:56.362134+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/YB6N55O4QIR2P2TRBZSKYVQZR3","json":"https://pith.science/pith/YB6N55O4QIR2P2TRBZSKYVQZR3.json","graph_json":"https://pith.science/api/pith-number/YB6N55O4QIR2P2TRBZSKYVQZR3/graph.json","events_json":"https://pith.science/api/pith-number/YB6N55O4QIR2P2TRBZSKYVQZR3/events.json","paper":"https://pith.science/paper/YB6N55O4"},"agent_actions":{"view_html":"https://pith.science/pith/YB6N55O4QIR2P2TRBZSKYVQZR3","download_json":"https://pith.science/pith/YB6N55O4QIR2P2TRBZSKYVQZR3.json","view_paper":"https://pith.science/paper/YB6N55O4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.01986&json=true","fetch_graph":"https://pith.science/api/pith-number/YB6N55O4QIR2P2TRBZSKYVQZR3/graph.json","fetch_events":"https://pith.science/api/pith-number/YB6N55O4QIR2P2TRBZSKYVQZR3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YB6N55O4QIR2P2TRBZSKYVQZR3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YB6N55O4QIR2P2TRBZSKYVQZR3/action/storage_attestation","attest_author":"https://pith.science/pith/YB6N55O4QIR2P2TRBZSKYVQZR3/action/author_attestation","sign_citation":"https://pith.science/pith/YB6N55O4QIR2P2TRBZSKYVQZR3/action/citation_signature","submit_replication":"https://pith.science/pith/YB6N55O4QIR2P2TRBZSKYVQZR3/action/replication_record"}},"created_at":"2026-05-18T00:38:25.057670+00:00","updated_at":"2026-05-18T00:38:25.057670+00:00"}