{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:E4EYGZVHLA27CPGO467NV24SZ7","short_pith_number":"pith:E4EYGZVH","canonical_record":{"source":{"id":"2304.06052","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-12T08:10:13Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2b4d40e6708cb852a3ae81ebba818e6b810811300ae67acb554b85d6cd9b22a8","abstract_canon_sha256":"fdcdb130b3fd707d8847681f614c0aa0e3cd03473a5301eceb0c426b396ae5b1"},"schema_version":"1.0"},"canonical_sha256":"27098366a75835f13ccee7bedaeb92cfd487eb47f2d7c4b3eeab84d66dea84c2","source":{"kind":"arxiv","id":"2304.06052","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.06052","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"arxiv_version","alias_value":"2304.06052v2","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.06052","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"pith_short_12","alias_value":"E4EYGZVHLA27","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"pith_short_16","alias_value":"E4EYGZVHLA27CPGO","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"pith_short_8","alias_value":"E4EYGZVH","created_at":"2026-07-05T06:42:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:E4EYGZVHLA27CPGO467NV24SZ7","target":"record","payload":{"canonical_record":{"source":{"id":"2304.06052","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-12T08:10:13Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2b4d40e6708cb852a3ae81ebba818e6b810811300ae67acb554b85d6cd9b22a8","abstract_canon_sha256":"fdcdb130b3fd707d8847681f614c0aa0e3cd03473a5301eceb0c426b396ae5b1"},"schema_version":"1.0"},"canonical_sha256":"27098366a75835f13ccee7bedaeb92cfd487eb47f2d7c4b3eeab84d66dea84c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:42:21.052073Z","signature_b64":"3LABYGM1Yn1mQd00cy2B/unfWQ3Jf3edcJo5u0zkDXW7P9U3egI61hnaQMisyGNUKwDf3avowp4wpC1QD5NKBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27098366a75835f13ccee7bedaeb92cfd487eb47f2d7c4b3eeab84d66dea84c2","last_reissued_at":"2026-07-05T06:42:21.051600Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:42:21.051600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.06052","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:42:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qwJJBSnsMOHeFlrlYFV9YGgLFwOz+z5hrbeJV0kTdkmHEN7q/jVrklZa5hAlrrMTuw/whF8XcIiBx1b33/8zCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T16:27:21.956073Z"},"content_sha256":"241232cd2c872fa8870aadb306085313d7028d9c1e09063a5e4c000b4e1d41cf","schema_version":"1.0","event_id":"sha256:241232cd2c872fa8870aadb306085313d7028d9c1e09063a5e4c000b4e1d41cf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:E4EYGZVHLA27CPGO467NV24SZ7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"ANITI), Florence De Grancey, L\\'eo And\\'eol (IMT, Luca Mossina, Thomas Fel","submitted_at":"2023-04-12T08:10:13Z","abstract_excerpt":"Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty. In this paper, we demonstrate the use of the conformal prediction framework to construct reliable and trustworthy predictors for detecting railway signals. Our approach is based on a novel dataset that includes images taken from the perspective of a train operator and state-of-the-art object detectors. We test several conformal approaches and introduce a new method based on conformal risk control. Our findings demonstrate the potential o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.06052","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.06052/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:42:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ViC2go6afgyudHEi0bx3ntCn7889i3VAet3Q2cSZJmbIqRkB8xBHPNd6NXMFFiEQuKwsHo4jiNswJqsFKgUSDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T16:27:21.956577Z"},"content_sha256":"e9086b4609a6f00056504fb3af1da6962f27412aa059ae12bc2631662028ac9d","schema_version":"1.0","event_id":"sha256:e9086b4609a6f00056504fb3af1da6962f27412aa059ae12bc2631662028ac9d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E4EYGZVHLA27CPGO467NV24SZ7/bundle.json","state_url":"https://pith.science/pith/E4EYGZVHLA27CPGO467NV24SZ7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E4EYGZVHLA27CPGO467NV24SZ7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-19T16:27:21Z","links":{"resolver":"https://pith.science/pith/E4EYGZVHLA27CPGO467NV24SZ7","bundle":"https://pith.science/pith/E4EYGZVHLA27CPGO467NV24SZ7/bundle.json","state":"https://pith.science/pith/E4EYGZVHLA27CPGO467NV24SZ7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E4EYGZVHLA27CPGO467NV24SZ7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:E4EYGZVHLA27CPGO467NV24SZ7","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":"fdcdb130b3fd707d8847681f614c0aa0e3cd03473a5301eceb0c426b396ae5b1","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-12T08:10:13Z","title_canon_sha256":"2b4d40e6708cb852a3ae81ebba818e6b810811300ae67acb554b85d6cd9b22a8"},"schema_version":"1.0","source":{"id":"2304.06052","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.06052","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"arxiv_version","alias_value":"2304.06052v2","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.06052","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"pith_short_12","alias_value":"E4EYGZVHLA27","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"pith_short_16","alias_value":"E4EYGZVHLA27CPGO","created_at":"2026-07-05T06:42:21Z"},{"alias_kind":"pith_short_8","alias_value":"E4EYGZVH","created_at":"2026-07-05T06:42:21Z"}],"graph_snapshots":[{"event_id":"sha256:e9086b4609a6f00056504fb3af1da6962f27412aa059ae12bc2631662028ac9d","target":"graph","created_at":"2026-07-05T06:42:21Z","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/2304.06052/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty. In this paper, we demonstrate the use of the conformal prediction framework to construct reliable and trustworthy predictors for detecting railway signals. Our approach is based on a novel dataset that includes images taken from the perspective of a train operator and state-of-the-art object detectors. We test several conformal approaches and introduce a new method based on conformal risk control. Our findings demonstrate the potential o","authors_text":"ANITI), Florence De Grancey, L\\'eo And\\'eol (IMT, Luca Mossina, Thomas Fel","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-12T08:10:13Z","title":"Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.06052","kind":"arxiv","version":2},"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":"sha256:241232cd2c872fa8870aadb306085313d7028d9c1e09063a5e4c000b4e1d41cf","target":"record","created_at":"2026-07-05T06:42:21Z","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":"fdcdb130b3fd707d8847681f614c0aa0e3cd03473a5301eceb0c426b396ae5b1","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-12T08:10:13Z","title_canon_sha256":"2b4d40e6708cb852a3ae81ebba818e6b810811300ae67acb554b85d6cd9b22a8"},"schema_version":"1.0","source":{"id":"2304.06052","kind":"arxiv","version":2}},"canonical_sha256":"27098366a75835f13ccee7bedaeb92cfd487eb47f2d7c4b3eeab84d66dea84c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"27098366a75835f13ccee7bedaeb92cfd487eb47f2d7c4b3eeab84d66dea84c2","first_computed_at":"2026-07-05T06:42:21.051600Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:42:21.051600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3LABYGM1Yn1mQd00cy2B/unfWQ3Jf3edcJo5u0zkDXW7P9U3egI61hnaQMisyGNUKwDf3avowp4wpC1QD5NKBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:42:21.052073Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.06052","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:241232cd2c872fa8870aadb306085313d7028d9c1e09063a5e4c000b4e1d41cf","sha256:e9086b4609a6f00056504fb3af1da6962f27412aa059ae12bc2631662028ac9d"],"state_sha256":"b9e7d6302e9376a41fcb9790746f09bc3278e988963a3a7e751272d4a4c95563"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QefHuDcnwDW2Bzd2BDN+rcBUisgLvWwpUmwlA8KTvQ854Fultd572GADvYuUyDRTBObqw84Pd2Aw5qCgjCoIAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T16:27:21.961800Z","bundle_sha256":"eb1cc57de429411ccc875a68bdb52ff988dcff01addc1f312cbd802e1b586e60"}}