{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ORP7EO4S2ARR2KGREP3LPTPL7M","short_pith_number":"pith:ORP7EO4S","schema_version":"1.0","canonical_sha256":"745ff23b92d0231d28d123f6b7cdebfb21fd6364531fae193448b93faa5f357a","source":{"kind":"arxiv","id":"2503.10886","version":1},"attestation_state":"computed","paper":{"title":"Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG","q-bio.PE"],"primary_cat":"cs.CV","authors_text":"Graham W. Taylor, Nathaniel Lesperance, Sujeevan Ratnasingham","submitted_at":"2025-03-13T21:18:10Z","abstract_excerpt":"In the context of pressing climate change challenges and the significant biodiversity loss among arthropods, automated taxonomic classification from organismal images is a subject of intense research. However, traditional AI pipelines based on deep neural visual architectures such as CNNs or ViTs face limitations such as degraded performance on the long-tail of classes and the inability to reason about their predictions. We integrate image captioning and retrieval-augmented generation (RAG) with large language models (LLMs) to enhance biodiversity monitoring, showing particular promise for cha"},"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":"2503.10886","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-13T21:18:10Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG","q-bio.PE"],"title_canon_sha256":"0614ec695b3253e7aa9383916a5cfaf2e3e5425adaee2b529284e320b3231937","abstract_canon_sha256":"a7d0b25faf33f2c90d4968acc62da5fe377d58c4cd126a6ebb415b95485729a0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:31:07.697342Z","signature_b64":"1JlWd3tvqLs8/zSr/xNrsitl9dJbXPLXMYL9pM7g6+oDqVNrDruypns0ITEGl/WxShS+7Bqib/kTVsjbywnzDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"745ff23b92d0231d28d123f6b7cdebfb21fd6364531fae193448b93faa5f357a","last_reissued_at":"2026-07-05T10:31:07.696420Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:31:07.696420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG","q-bio.PE"],"primary_cat":"cs.CV","authors_text":"Graham W. Taylor, Nathaniel Lesperance, Sujeevan Ratnasingham","submitted_at":"2025-03-13T21:18:10Z","abstract_excerpt":"In the context of pressing climate change challenges and the significant biodiversity loss among arthropods, automated taxonomic classification from organismal images is a subject of intense research. However, traditional AI pipelines based on deep neural visual architectures such as CNNs or ViTs face limitations such as degraded performance on the long-tail of classes and the inability to reason about their predictions. We integrate image captioning and retrieval-augmented generation (RAG) with large language models (LLMs) to enhance biodiversity monitoring, showing particular promise for cha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.10886","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/2503.10886/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":"2503.10886","created_at":"2026-07-05T10:31:07.696540+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.10886v1","created_at":"2026-07-05T10:31:07.696540+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.10886","created_at":"2026-07-05T10:31:07.696540+00:00"},{"alias_kind":"pith_short_12","alias_value":"ORP7EO4S2ARR","created_at":"2026-07-05T10:31:07.696540+00:00"},{"alias_kind":"pith_short_16","alias_value":"ORP7EO4S2ARR2KGR","created_at":"2026-07-05T10:31:07.696540+00:00"},{"alias_kind":"pith_short_8","alias_value":"ORP7EO4S","created_at":"2026-07-05T10:31:07.696540+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/ORP7EO4S2ARR2KGREP3LPTPL7M","json":"https://pith.science/pith/ORP7EO4S2ARR2KGREP3LPTPL7M.json","graph_json":"https://pith.science/api/pith-number/ORP7EO4S2ARR2KGREP3LPTPL7M/graph.json","events_json":"https://pith.science/api/pith-number/ORP7EO4S2ARR2KGREP3LPTPL7M/events.json","paper":"https://pith.science/paper/ORP7EO4S"},"agent_actions":{"view_html":"https://pith.science/pith/ORP7EO4S2ARR2KGREP3LPTPL7M","download_json":"https://pith.science/pith/ORP7EO4S2ARR2KGREP3LPTPL7M.json","view_paper":"https://pith.science/paper/ORP7EO4S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.10886&json=true","fetch_graph":"https://pith.science/api/pith-number/ORP7EO4S2ARR2KGREP3LPTPL7M/graph.json","fetch_events":"https://pith.science/api/pith-number/ORP7EO4S2ARR2KGREP3LPTPL7M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ORP7EO4S2ARR2KGREP3LPTPL7M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ORP7EO4S2ARR2KGREP3LPTPL7M/action/storage_attestation","attest_author":"https://pith.science/pith/ORP7EO4S2ARR2KGREP3LPTPL7M/action/author_attestation","sign_citation":"https://pith.science/pith/ORP7EO4S2ARR2KGREP3LPTPL7M/action/citation_signature","submit_replication":"https://pith.science/pith/ORP7EO4S2ARR2KGREP3LPTPL7M/action/replication_record"}},"created_at":"2026-07-05T10:31:07.696540+00:00","updated_at":"2026-07-05T10:31:07.696540+00:00"}