{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MTDQRZDV7CBFJZX7CJ2WIYL64R","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3cf88f7b36dedbe52c64e4f7466f86c4f423280c6aa7367c88ade4d1df542fcc","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-23T13:59:02Z","title_canon_sha256":"a9aca8495cf40d902aa0ab95664f75f7542d5f72182d0925798a7815cd9f169b"},"schema_version":"1.0","source":{"id":"2505.17921","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.17921","created_at":"2026-07-05T11:08:33Z"},{"alias_kind":"arxiv_version","alias_value":"2505.17921v1","created_at":"2026-07-05T11:08:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17921","created_at":"2026-07-05T11:08:33Z"},{"alias_kind":"pith_short_12","alias_value":"MTDQRZDV7CBF","created_at":"2026-07-05T11:08:33Z"},{"alias_kind":"pith_short_16","alias_value":"MTDQRZDV7CBFJZX7","created_at":"2026-07-05T11:08:33Z"},{"alias_kind":"pith_short_8","alias_value":"MTDQRZDV","created_at":"2026-07-05T11:08:33Z"}],"graph_snapshots":[{"event_id":"sha256:d9d5e34087348c7b3ace0b183357b9099a02012fc1768abfc90e6997d1284ca6","target":"graph","created_at":"2026-07-05T11:08:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.17921/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Determining the type of kidney stones is crucial for prescribing appropriate treatments to prevent recurrence. Currently, various approaches exist to identify the type of kidney stones. However, obtaining results through the reference ex vivo identification procedure can take several weeks, while in vivo visual recognition requires highly trained specialists. For this reason, deep learning models have been developed to provide urologists with an automated classification of kidney stones during ureteroscopies. Nevertheless, a common issue with these models is the lack of training data. This con","authors_text":"Carlos Salazar-Ruiz, Christian Daul, Clement Larose, Francisco Lopez-Tiro, Gilberto Ochoa-Ruiz, Ivan Reyes-Amezcua","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-23T13:59:02Z","title":"Evaluation of Few-Shot Learning Methods for Kidney Stone Type Recognition in Ureteroscopy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17921","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1daa72169b61f823c3f73bc22f604331bf4b4f91ac1a1f78e72a956f761d0a4f","target":"record","created_at":"2026-07-05T11:08:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3cf88f7b36dedbe52c64e4f7466f86c4f423280c6aa7367c88ade4d1df542fcc","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-23T13:59:02Z","title_canon_sha256":"a9aca8495cf40d902aa0ab95664f75f7542d5f72182d0925798a7815cd9f169b"},"schema_version":"1.0","source":{"id":"2505.17921","kind":"arxiv","version":1}},"canonical_sha256":"64c708e475f88254e6ff127564617ee4404d75f396ddf77cf691a0d408fa5567","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"64c708e475f88254e6ff127564617ee4404d75f396ddf77cf691a0d408fa5567","first_computed_at":"2026-07-05T11:08:33.858867Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:08:33.858867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GLaGrBOy9LmEsCeuvFG5Y9l8eVVujjs3oH1VRRKBSlR4uI20cSGXkusQ8bPLnd1Dl4jybhlKHK3JoTGGZP5yAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:08:33.859304Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.17921","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1daa72169b61f823c3f73bc22f604331bf4b4f91ac1a1f78e72a956f761d0a4f","sha256:d9d5e34087348c7b3ace0b183357b9099a02012fc1768abfc90e6997d1284ca6"],"state_sha256":"8e6f19bae457f63eef518d7ce1e6d8bec82494a90e3b03e7bdb941eb58d8ce22"}