{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:4QAQ55EJWLNOWIAZKRWY5FPFA3","short_pith_number":"pith:4QAQ55EJ","canonical_record":{"source":{"id":"2407.07818","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-10T16:43:14Z","cross_cats_sorted":[],"title_canon_sha256":"7046075db4e1d7d007f25151f204ccd198dab7b55f3605ebade1c490a7292ab9","abstract_canon_sha256":"5b71dcd7d863eb7b6a180e6e5237a9273adaebf2b9689a3591ba3970eda759c2"},"schema_version":"1.0"},"canonical_sha256":"e4010ef489b2daeb2019546d8e95e506d484d18a39082a5091996053b67aff02","source":{"kind":"arxiv","id":"2407.07818","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.07818","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"arxiv_version","alias_value":"2407.07818v3","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.07818","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"pith_short_12","alias_value":"4QAQ55EJWLNO","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"pith_short_16","alias_value":"4QAQ55EJWLNOWIAZ","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"pith_short_8","alias_value":"4QAQ55EJ","created_at":"2026-07-05T08:54:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:4QAQ55EJWLNOWIAZKRWY5FPFA3","target":"record","payload":{"canonical_record":{"source":{"id":"2407.07818","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-10T16:43:14Z","cross_cats_sorted":[],"title_canon_sha256":"7046075db4e1d7d007f25151f204ccd198dab7b55f3605ebade1c490a7292ab9","abstract_canon_sha256":"5b71dcd7d863eb7b6a180e6e5237a9273adaebf2b9689a3591ba3970eda759c2"},"schema_version":"1.0"},"canonical_sha256":"e4010ef489b2daeb2019546d8e95e506d484d18a39082a5091996053b67aff02","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:53.885108Z","signature_b64":"fIwe7JSP/V7Xd2DwUFiK6NNbY/U0qzK6zPb0UZC3jwMHDbOfgSIV486Cmxn4W6+9UjWHvhg5NQR7URqHqdBnBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4010ef489b2daeb2019546d8e95e506d484d18a39082a5091996053b67aff02","last_reissued_at":"2026-07-05T08:54:53.884643Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:53.884643Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.07818","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:54:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WCxyyQ5zWk9vEopPmQAj1tMoopbG3KcpVkxVOZrifivD1VmmcH/7X/684MYL+jHW0/jM0wg3EScrubaigObvCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:41:48.128993Z"},"content_sha256":"c47f8ebdc9b023879f3a62d1a2b77a6d0ba0d6aa7b4ec4fa743a369abecf857c","schema_version":"1.0","event_id":"sha256:c47f8ebdc9b023879f3a62d1a2b77a6d0ba0d6aa7b4ec4fa743a369abecf857c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:4QAQ55EJWLNOWIAZKRWY5FPFA3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Artur Garcez, Daniel Sikar, Dany Laksono, Kaleem Peeroo, Maeve Hutchinson, Mirela Reljan-Delaney, Naman Singh, Robin Bloomfield, Tillman Weyde","submitted_at":"2024-07-10T16:43:14Z","abstract_excerpt":"This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM is obtained by leveraging softmax outputs and clustering techniques to measure the distances between the predictions of a trained neural network and class centroids. By analyzing these distances, the MLM provides a comprehensive view of the model's misclassification tendencies, enabling decision-makers to identify the most common and critical sources of errors. The MLM allows for the prioritization of model improvem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.07818","kind":"arxiv","version":3},"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/2407.07818/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":"2026-07-05T08:54:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q6mdaSF95irt0NFmMh6Elto2n4vx2PshXHEM8ijwDjs9OIgfYFEWktuKE57rTUwiP3BimeeBSw5gr82yZvfVCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:41:48.129883Z"},"content_sha256":"5389baadb7d83c8b2fb3ddb41acd242086f10f6f7587bb84c973935f4dc6af72","schema_version":"1.0","event_id":"sha256:5389baadb7d83c8b2fb3ddb41acd242086f10f6f7587bb84c973935f4dc6af72"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4QAQ55EJWLNOWIAZKRWY5FPFA3/bundle.json","state_url":"https://pith.science/pith/4QAQ55EJWLNOWIAZKRWY5FPFA3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4QAQ55EJWLNOWIAZKRWY5FPFA3/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":"2026-08-06T16:41:48Z","links":{"resolver":"https://pith.science/pith/4QAQ55EJWLNOWIAZKRWY5FPFA3","bundle":"https://pith.science/pith/4QAQ55EJWLNOWIAZKRWY5FPFA3/bundle.json","state":"https://pith.science/pith/4QAQ55EJWLNOWIAZKRWY5FPFA3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4QAQ55EJWLNOWIAZKRWY5FPFA3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4QAQ55EJWLNOWIAZKRWY5FPFA3","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":"5b71dcd7d863eb7b6a180e6e5237a9273adaebf2b9689a3591ba3970eda759c2","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-10T16:43:14Z","title_canon_sha256":"7046075db4e1d7d007f25151f204ccd198dab7b55f3605ebade1c490a7292ab9"},"schema_version":"1.0","source":{"id":"2407.07818","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.07818","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"arxiv_version","alias_value":"2407.07818v3","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.07818","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"pith_short_12","alias_value":"4QAQ55EJWLNO","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"pith_short_16","alias_value":"4QAQ55EJWLNOWIAZ","created_at":"2026-07-05T08:54:53Z"},{"alias_kind":"pith_short_8","alias_value":"4QAQ55EJ","created_at":"2026-07-05T08:54:53Z"}],"graph_snapshots":[{"event_id":"sha256:5389baadb7d83c8b2fb3ddb41acd242086f10f6f7587bb84c973935f4dc6af72","target":"graph","created_at":"2026-07-05T08:54:53Z","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/2407.07818/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM is obtained by leveraging softmax outputs and clustering techniques to measure the distances between the predictions of a trained neural network and class centroids. By analyzing these distances, the MLM provides a comprehensive view of the model's misclassification tendencies, enabling decision-makers to identify the most common and critical sources of errors. The MLM allows for the prioritization of model improvem","authors_text":"Artur Garcez, Daniel Sikar, Dany Laksono, Kaleem Peeroo, Maeve Hutchinson, Mirela Reljan-Delaney, Naman Singh, Robin Bloomfield, Tillman Weyde","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-10T16:43:14Z","title":"The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.07818","kind":"arxiv","version":3},"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:c47f8ebdc9b023879f3a62d1a2b77a6d0ba0d6aa7b4ec4fa743a369abecf857c","target":"record","created_at":"2026-07-05T08:54:53Z","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":"5b71dcd7d863eb7b6a180e6e5237a9273adaebf2b9689a3591ba3970eda759c2","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-10T16:43:14Z","title_canon_sha256":"7046075db4e1d7d007f25151f204ccd198dab7b55f3605ebade1c490a7292ab9"},"schema_version":"1.0","source":{"id":"2407.07818","kind":"arxiv","version":3}},"canonical_sha256":"e4010ef489b2daeb2019546d8e95e506d484d18a39082a5091996053b67aff02","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4010ef489b2daeb2019546d8e95e506d484d18a39082a5091996053b67aff02","first_computed_at":"2026-07-05T08:54:53.884643Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:54:53.884643Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fIwe7JSP/V7Xd2DwUFiK6NNbY/U0qzK6zPb0UZC3jwMHDbOfgSIV486Cmxn4W6+9UjWHvhg5NQR7URqHqdBnBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:54:53.885108Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.07818","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c47f8ebdc9b023879f3a62d1a2b77a6d0ba0d6aa7b4ec4fa743a369abecf857c","sha256:5389baadb7d83c8b2fb3ddb41acd242086f10f6f7587bb84c973935f4dc6af72"],"state_sha256":"773b0b4583b262ad197a86948efb03e4a9f4223159457bb56d9db1901987dd69"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v7Lr43KOG04yxB+AxcgeIArpGgDmmCQ6VU+2OfUw7Zau4Ni0Ra74+0E2Ju8ddvP/PVgo++G6BFntwCqGWUQWCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T16:41:48.134330Z","bundle_sha256":"ae08822d1415b7d8d4ec5864d38597eb6220ee5387a08ee74a5b65e932cafd92"}}