{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:W6T2Q3RO444HIRIRU2XD5JFV55","short_pith_number":"pith:W6T2Q3RO","canonical_record":{"source":{"id":"2606.24974","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-23T11:47:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c642a615b295574cc9e832212d54651384816751dd112add77b5e0ddc03f74fc","abstract_canon_sha256":"fddf27bbcbc33b47e89b2bb107e3692a90192d08fa316cb7b6410321b5fc77e8"},"schema_version":"1.0"},"canonical_sha256":"b7a7a86e2ee738744511a6ae3ea4b5ef43d9bb9e5d0393df49a51f70453addb0","source":{"kind":"arxiv","id":"2606.24974","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.24974","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"arxiv_version","alias_value":"2606.24974v1","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24974","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"pith_short_12","alias_value":"W6T2Q3RO444H","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"pith_short_16","alias_value":"W6T2Q3RO444HIRIR","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"pith_short_8","alias_value":"W6T2Q3RO","created_at":"2026-06-25T00:17:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:W6T2Q3RO444HIRIRU2XD5JFV55","target":"record","payload":{"canonical_record":{"source":{"id":"2606.24974","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-23T11:47:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c642a615b295574cc9e832212d54651384816751dd112add77b5e0ddc03f74fc","abstract_canon_sha256":"fddf27bbcbc33b47e89b2bb107e3692a90192d08fa316cb7b6410321b5fc77e8"},"schema_version":"1.0"},"canonical_sha256":"b7a7a86e2ee738744511a6ae3ea4b5ef43d9bb9e5d0393df49a51f70453addb0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T00:17:47.272156Z","signature_b64":"6CejcY2e9ePr0h4KSm+a0VoHkZV85AAmlNRLkIByTFPJHHHpIwOx5wJJue3t8j4Zo/6uydwk2A0AfWaGrIL5CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7a7a86e2ee738744511a6ae3ea4b5ef43d9bb9e5d0393df49a51f70453addb0","last_reissued_at":"2026-06-25T00:17:47.271764Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T00:17:47.271764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.24974","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-25T00:17:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NMHiwJrwgx3TUXZj/h4rm9Uh7ehUymdCwCUv76Zwib1FkQDnIAYtl8pQdwb7YhY3YFG6CZEW2IGuvaRsVW7WAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T04:12:21.292189Z"},"content_sha256":"3a7e97d974c41e08a9e2f82d558a04e869cf06e1354496268125e0ebf402f461","schema_version":"1.0","event_id":"sha256:3a7e97d974c41e08a9e2f82d558a04e869cf06e1354496268125e0ebf402f461"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:W6T2Q3RO444HIRIRU2XD5JFV55","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"David A. Kelly, Nathan Blake","submitted_at":"2026-06-23T11:47:29Z","abstract_excerpt":"Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to be trusted and decisions justified. Explainability (XAI) tools use heuristics which often add signal noise to the explanation \"core\". It is not always obvious what is signal from the model and what is noise from the XAI. We propose the use of spectral entropy as a measure of noise in XAI output. We demonstrate its usefulness in the context of classifying arrhythmias in an ECG dataset with different post hoc explainability techniques."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24974","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/2606.24974/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":"2026-06-25T00:17:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u/pCklWyOxUsunO1MsZxgV0QwQ3ncddvEQuxyJY3jVj0A2qjIHUNE2e6OFr9TQydkeO0Lrbhg/kD4xjuFAo4AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T04:12:21.292687Z"},"content_sha256":"c18024e3d163e6d5076550ff19d51721d06dc236ba1300a4aeea3e598a345ff7","schema_version":"1.0","event_id":"sha256:c18024e3d163e6d5076550ff19d51721d06dc236ba1300a4aeea3e598a345ff7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W6T2Q3RO444HIRIRU2XD5JFV55/bundle.json","state_url":"https://pith.science/pith/W6T2Q3RO444HIRIRU2XD5JFV55/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W6T2Q3RO444HIRIRU2XD5JFV55/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":"2026-08-21T04:12:21Z","links":{"resolver":"https://pith.science/pith/W6T2Q3RO444HIRIRU2XD5JFV55","bundle":"https://pith.science/pith/W6T2Q3RO444HIRIRU2XD5JFV55/bundle.json","state":"https://pith.science/pith/W6T2Q3RO444HIRIRU2XD5JFV55/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W6T2Q3RO444HIRIRU2XD5JFV55/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:W6T2Q3RO444HIRIRU2XD5JFV55","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":"fddf27bbcbc33b47e89b2bb107e3692a90192d08fa316cb7b6410321b5fc77e8","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-23T11:47:29Z","title_canon_sha256":"c642a615b295574cc9e832212d54651384816751dd112add77b5e0ddc03f74fc"},"schema_version":"1.0","source":{"id":"2606.24974","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.24974","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"arxiv_version","alias_value":"2606.24974v1","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24974","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"pith_short_12","alias_value":"W6T2Q3RO444H","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"pith_short_16","alias_value":"W6T2Q3RO444HIRIR","created_at":"2026-06-25T00:17:47Z"},{"alias_kind":"pith_short_8","alias_value":"W6T2Q3RO","created_at":"2026-06-25T00:17:47Z"}],"graph_snapshots":[{"event_id":"sha256:c18024e3d163e6d5076550ff19d51721d06dc236ba1300a4aeea3e598a345ff7","target":"graph","created_at":"2026-06-25T00:17:47Z","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/2606.24974/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to be trusted and decisions justified. Explainability (XAI) tools use heuristics which often add signal noise to the explanation \"core\". It is not always obvious what is signal from the model and what is noise from the XAI. We propose the use of spectral entropy as a measure of noise in XAI output. We demonstrate its usefulness in the context of classifying arrhythmias in an ECG dataset with different post hoc explainability techniques.","authors_text":"David A. Kelly, Nathan Blake","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-23T11:47:29Z","title":"Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24974","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":"sha256:3a7e97d974c41e08a9e2f82d558a04e869cf06e1354496268125e0ebf402f461","target":"record","created_at":"2026-06-25T00:17:47Z","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":"fddf27bbcbc33b47e89b2bb107e3692a90192d08fa316cb7b6410321b5fc77e8","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-23T11:47:29Z","title_canon_sha256":"c642a615b295574cc9e832212d54651384816751dd112add77b5e0ddc03f74fc"},"schema_version":"1.0","source":{"id":"2606.24974","kind":"arxiv","version":1}},"canonical_sha256":"b7a7a86e2ee738744511a6ae3ea4b5ef43d9bb9e5d0393df49a51f70453addb0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b7a7a86e2ee738744511a6ae3ea4b5ef43d9bb9e5d0393df49a51f70453addb0","first_computed_at":"2026-06-25T00:17:47.271764Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-25T00:17:47.271764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6CejcY2e9ePr0h4KSm+a0VoHkZV85AAmlNRLkIByTFPJHHHpIwOx5wJJue3t8j4Zo/6uydwk2A0AfWaGrIL5CA==","signature_status":"signed_v1","signed_at":"2026-06-25T00:17:47.272156Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.24974","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3a7e97d974c41e08a9e2f82d558a04e869cf06e1354496268125e0ebf402f461","sha256:c18024e3d163e6d5076550ff19d51721d06dc236ba1300a4aeea3e598a345ff7"],"state_sha256":"600b83255f29da7391b9f6aba5bad54044f69621aa96f7df3775368ea4edbb5d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DyDvWy+JEYs/U3bofMJPA71E4dYzuOwzkMphwCbp42LW+I+K3tfm1KqBiIGJGWmZPeopNBh/osKUr/qXfAXWBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T04:12:21.296609Z","bundle_sha256":"d03bd6fc5f2df2ce72bb963bafe7d088a48c2871574a1f2403f7776a80a41166"}}