{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","short_pith_number":"pith:JTJB3GT3","canonical_record":{"source":{"id":"2605.16232","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-15T17:42:11Z","cross_cats_sorted":["cs.AI","cs.ET","cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"c51e08ec920687b178af2d86c507a6891e32361e26eaa8118b69bd21d05b08bf","abstract_canon_sha256":"ab97311ec1655c98171109a048531957f657869ace788f6958ce6240c4cf4844"},"schema_version":"1.0"},"canonical_sha256":"4cd21d9a7b078ef9e47d1df69588c1b0aba72d8dd760313296dab3db0fbf32bf","source":{"kind":"arxiv","id":"2605.16232","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16232","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16232v1","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16232","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"pith_short_12","alias_value":"JTJB3GT3A6HP","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"pith_short_16","alias_value":"JTJB3GT3A6HPTZD5","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"pith_short_8","alias_value":"JTJB3GT3","created_at":"2026-05-20T00:01:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16232","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-15T17:42:11Z","cross_cats_sorted":["cs.AI","cs.ET","cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"c51e08ec920687b178af2d86c507a6891e32361e26eaa8118b69bd21d05b08bf","abstract_canon_sha256":"ab97311ec1655c98171109a048531957f657869ace788f6958ce6240c4cf4844"},"schema_version":"1.0"},"canonical_sha256":"4cd21d9a7b078ef9e47d1df69588c1b0aba72d8dd760313296dab3db0fbf32bf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:59.220438Z","signature_b64":"KpeAi+KIDTESgSEjToQa4/U+QEdri3zfqEalZvS7yzGqNqyZqStZqyZQgD5aHTzRKUf7I5r2uXWPG5MvQnWcCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4cd21d9a7b078ef9e47d1df69588c1b0aba72d8dd760313296dab3db0fbf32bf","last_reissued_at":"2026-05-20T00:01:59.219603Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:59.219603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16232","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":"2026-05-20T00:01:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bwQYqz2W8mMC2tgiDyOYL7AYIwXqWNssT8cuZvUA4IHlTbJa62/Rj5dRU5jHHD/r5zVfS6QXRuDJPttU/gQHDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:53:03.095048Z"},"content_sha256":"ddc918dd694a16ab5990315171c568ed9349cd51b587b3f72d06c48e19e33f6a","schema_version":"1.0","event_id":"sha256:ddc918dd694a16ab5990315171c568ed9349cd51b587b3f72d06c48e19e33f6a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Unified Generative-AI Framework for Smart Energy Infrastructure: Intelligent Gas Distribution, Utility Billing, Carbon Analytics, and Quantum-Inspired Optimisation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.CL","authors_text":"Pavan Manjunath, Thomas pruefer","submitted_at":"2026-05-15T17:42:11Z","abstract_excerpt":"The accelerating convergence of smart metering, generative artificial intelligence, and quantum-inspired combinatorial optimisation is reshaping how energy utilities manage physical infrastructure, customer engagement, and environmental accountability"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16232","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/2605.16232/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"shingle_duplication","ran_at":"2026-05-19T17:49:42.200380Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T17:49:41.809337Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:23.124922Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"external_links","ran_at":"2026-05-19T17:31:27.633037Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.622560Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T16:51:58.121564Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"19b88cbbd8e6fae8db2af839e67de911bc00d16a1b41decc53fb1404af1716ac"},"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":"2026-05-20T00:01:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"41BwA0BdObMdLu7G4nDajELlGPlgDqhjrmrdC8D/lg9xkaUex8VnY7TfUOtKPG2VSK1giOpDkKO0CkfuEJKbAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:53:03.096019Z"},"content_sha256":"ec005f86e212333ad7d4faa264dce63d00584d771810e7704e52d8c337c0a02d","schema_version":"1.0","event_id":"sha256:ec005f86e212333ad7d4faa264dce63d00584d771810e7704e52d8c337c0a02d"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","target":"integrity","payload":{"note":"Identifier '10.1109/tii.2023.3301234' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Chen, T., Wang, S., Yu, J., Li, R.: Graph-attention autoencoder for multi-modal pipeline anomaly detection. IEEE Transactions on Industrial Informatics 20(3), 3210 –3221 (2024). https://doi.org/10.1109/TII.2023.3301234","arxiv_id":"2605.16232","detector":"doi_compliance","evidence":{"doi":"10.1109/tii.2023.3301234","arxiv_id":null,"ref_index":43,"raw_excerpt":"Chen, T., Wang, S., Yu, J., Li, R.: Graph-attention autoencoder for multi-modal pipeline anomaly detection. IEEE Transactions on Industrial Informatics 20(3), 3210 –3221 (2024). https://doi.org/10.1109/TII.2023.3301234","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":43,"audited_at":"2026-05-20T18:32:53.958343Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/tii.2023.3301234","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"a9fce4bcbb01eaedcadfef6a0b57e3d2069dd4cea18a459156ec5ab8d6628e0b","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":5407,"payload_sha256":"5f1859f1378836044217ed66b582625bf7b898753accd1f22465918cc51e6b43","signature_b64":"GMkw+J+jc1jxMkVHZGZQ2X2MhL5ZPlxNbuNqsCL29cZFBeSs27dZWTJ8/5KVa9vJAQ0X/72b7l2PvJ+MqxdyCw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T18:33:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6k6RhS1Og/wRPWZ768D7AGYAjJaJFf+MLYxOWjXCWzn6g5uiUhCzU0Z70muPE8UjmnNviNZMRJmWaa2ASyh2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:53:03.098080Z"},"content_sha256":"8793a9445c9490a8e4c686ffe5d1a056d117618e22d87135a6b29fcdb0675e2c","schema_version":"1.0","event_id":"sha256:8793a9445c9490a8e4c686ffe5d1a056d117618e22d87135a6b29fcdb0675e2c"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","target":"integrity","payload":{"note":"Identifier '10.1109/tii.2022.3148302' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Ravula, S., Hu, X., Karam, L.J.: Graph -neural-network-based pipeline leakage localisation using pressure and flow sensors. IEEE Transactions on Industrial Informatics 18(11), 7891 –7900 (2022). https://doi.org/10.1109/TII.2022.3148302","arxiv_id":"2605.16232","detector":"doi_compliance","evidence":{"doi":"10.1109/tii.2022.3148302","arxiv_id":null,"ref_index":42,"raw_excerpt":"Ravula, S., Hu, X., Karam, L.J.: Graph -neural-network-based pipeline leakage localisation using pressure and flow sensors. IEEE Transactions on Industrial Informatics 18(11), 7891 –7900 (2022). https://doi.org/10.1109/TII.2022.3148302","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":42,"audited_at":"2026-05-20T18:32:53.958343Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/tii.2022.3148302","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"e8af1487ffd1e3a22ff7ee338b79245a02bc2b5c0f1fb7b515a4df26781348f5","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":5406,"payload_sha256":"2bed75b4644f21923c0a127cdd4f9e1b11174f362dc4b4ae2203b8e90e6a197e","signature_b64":"pWMFRVbg8LdNdL6zEXbSVUVozTaiMASASSzjGJXPlGtASg00ZzbmfJTrxV1vzs/kvmLUH/b0z1k5ju1HIlACAw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T18:33:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rqHII+wc6orB1b+eeop+CuvSG9uRQs3fSbm9A+GUxjj3e1n9eAcqcgIbZ8FmRHjivgqnnKuyCyqIIlaXpeaWDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:53:03.098837Z"},"content_sha256":"3bea4601e62857338b1b8e560fe20bc254f76fe7c09b6d4eb5518a590f2c27e0","schema_version":"1.0","event_id":"sha256:3bea4601e62857338b1b8e560fe20bc254f76fe7c09b6d4eb5518a590f2c27e0"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","target":"integrity","payload":{"note":"Identifier '10.1063/5.0052879' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Haben, S., Arora, S., Giasemidis, G., Voss, M., Vukadinovic Greetham, D.: Review of low voltage load forecasting with the focus on distributed energy resources. Journal of Renewable and Sustainable Energy 13(4), 043703 (2021). https://doi.o","arxiv_id":"2605.16232","detector":"doi_compliance","evidence":{"doi":"10.1063/5.0052879","arxiv_id":null,"ref_index":37,"raw_excerpt":"Haben, S., Arora, S., Giasemidis, G., Voss, M., Vukadinovic Greetham, D.: Review of low voltage load forecasting with the focus on distributed energy resources. Journal of Renewable and Sustainable Energy 13(4), 043703 (2021). https://doi.org/10.1063/5.0052879","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":37,"audited_at":"2026-05-20T18:32:53.958343Z","event_type":"pith.integrity.v1","detected_doi":"10.1063/5.0052879","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"13d661137ab87ce6a19c352e75a1e84fda5c9af934091bbf1438cbb35a1dda3d","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":5405,"payload_sha256":"df1dcb49f9e09523aa5a5a06c4ae495ad5ecb9a831f13e7094eb22d5ec91121f","signature_b64":"17QQjpe01qad6xkrwUVB8QQ9LHmp8PL9gCnhSMkx1bCfh70/gvbBCvUcpZft63zCalbGqs5PdWuhsaBL4/ZqCQ==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T18:33:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4oBIxQ0cKxgcr2+dbFU0XPIDTlVNgaGQ9ojCZajec7I5+yJ2yfTCpJJycHWHTL4p0N/EFlk8Pjv5JKQU2TM4Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:53:03.099395Z"},"content_sha256":"fcb32965dd16a495c64da9bdd5b415ab33d1959e88db48cdc7d8b86a7ee9ee7c","schema_version":"1.0","event_id":"sha256:fcb32965dd16a495c64da9bdd5b415ab33d1959e88db48cdc7d8b86a7ee9ee7c"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","target":"integrity","payload":{"note":"Identifier '10.1109/tpwrs.2021.3128667' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Liao, Z., Zhao, Z., Luo, Z., et al.: Deep generative models for distribution system state estimation. IEEE Transactions on Power Systems 37(4), 3174–3184 (2022). https://doi.org/10.1109/TPWRS.2021.3128667","arxiv_id":"2605.16232","detector":"doi_compliance","evidence":{"doi":"10.1109/tpwrs.2021.3128667","arxiv_id":null,"ref_index":28,"raw_excerpt":"Liao, Z., Zhao, Z., Luo, Z., et al.: Deep generative models for distribution system state estimation. IEEE Transactions on Power Systems 37(4), 3174–3184 (2022). https://doi.org/10.1109/TPWRS.2021.3128667","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":28,"audited_at":"2026-05-20T18:32:53.958343Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/tpwrs.2021.3128667","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"fec87e188caaffecc84f35a30aeaafe93d87b8469fd11a5402ef7cc49e7f9a8e","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":5404,"payload_sha256":"0985d1d7ec898c899cc3b8f2045a6267a0cedc4731ab64c05c5b452f5d0fa9d8","signature_b64":"s1J7sdYjd0AMegBM9DqfmNdJ8AVpfdUaX1Tj4ErixHQdiNdSc3sPnSbcxphNTg7SZWBd/HWET6M3ESWX2E7hAg==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T18:33:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kX8G5xm0f+vXo/X3g021z032YP5SyPfUBzO8OC5upbSIfLa2wKyW0SBGE8HmNspfrA9JMDgysLsPRHTBkTTwCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:53:03.099975Z"},"content_sha256":"fce8df970a52ff5a687a16587113466b2158b2683fe1d9518cad15dcf5924abd","schema_version":"1.0","event_id":"sha256:fce8df970a52ff5a687a16587113466b2158b2683fe1d9518cad15dcf5924abd"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","target":"integrity","payload":{"note":"Identifier '10.1109/tsg.2014.2356700' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Eibl, G., Engel, D.: A systematic analysis of privacy in smart metering. IEEE Transactions on Smart Grid 6(2), 786–794 (2015). https://doi.org/10.1109/TSG.2014.2356700","arxiv_id":"2605.16232","detector":"doi_compliance","evidence":{"doi":"10.1109/tsg.2014.2356700","arxiv_id":null,"ref_index":13,"raw_excerpt":"Eibl, G., Engel, D.: A systematic analysis of privacy in smart metering. IEEE Transactions on Smart Grid 6(2), 786–794 (2015). https://doi.org/10.1109/TSG.2014.2356700","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":13,"audited_at":"2026-05-20T18:32:53.958343Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/tsg.2014.2356700","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"36d46f5d96fb578a824db876528609002cf4b9f249bc2dd1282d3f30b662c748","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":5403,"payload_sha256":"6a33841f7677859679651a0fa186154ae4cc55ed8eba37dbee1d59de4e6ce477","signature_b64":"YDL7ImMzsJIaN1YT1BSv5H2qdDqsYV5DW3tOpcxCSCMu+Kc7PpcX0XEHmrzWkEjxYw3W6pjpQy8OzEGwAWx5DA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T18:33:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f21l5N2f+SnsvQOEF7LB5rE91idlGlwLUDCv7VvUAQV6/CQrXnvvPGMTFO2px3mHv/3wBegTOzop3d04RbrHDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:53:03.100554Z"},"content_sha256":"ecd066cdde4a70a2dbb8a639391786bcf787a8b942fa0f7d501c6517312fa762","schema_version":"1.0","event_id":"sha256:ecd066cdde4a70a2dbb8a639391786bcf787a8b942fa0f7d501c6517312fa762"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","target":"integrity","payload":{"note":"Identifier '10.1016/j.apenergy.2023.121082' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Jiang, Z., Zheng, X., Liu, X.: Carbon -aware compressor scheduling in gas distribution networks using grid carbon-intensity feeds. Applied Energy 341, 121082 (2023). https://doi.org/10.1016/j.apenergy.2023.121082","arxiv_id":"2605.16232","detector":"doi_compliance","evidence":{"doi":"10.1016/j.apenergy.2023.121082","arxiv_id":null,"ref_index":10,"raw_excerpt":"Jiang, Z., Zheng, X., Liu, X.: Carbon -aware compressor scheduling in gas distribution networks using grid carbon-intensity feeds. Applied Energy 341, 121082 (2023). https://doi.org/10.1016/j.apenergy.2023.121082","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":10,"audited_at":"2026-05-20T18:32:53.958343Z","event_type":"pith.integrity.v1","detected_doi":"10.1016/j.apenergy.2023.121082","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"0d39e7c920762f87626721da5bdc389cabf0ee3a40c5a6adee34589498b8e0c3","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":5402,"payload_sha256":"c23452a1be9ad595a8b4fffb414fc5e0e6cb1d68dabadce75ea8f6a8f2f81f12","signature_b64":"cvrkWyPZmw4cmDwEGr2D9eBwgrMxulmnC3bovup37eSeX1aCiEMsAQ4yTvphv0A0IdWgo1FUpQ+B+6XXJtlaAg==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T18:33:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DG2hY9oMGw7i3AwZeggS7hhQQQVLUDbVc36vOW9qBWwC+CkijAmNyMktT2q1A9vBbdXD0DpGdz4BThePX89/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:53:03.101145Z"},"content_sha256":"4e14fec26f37affbe578af5f71356995a64d3e79bb4634dd791784743ee5b10c","schema_version":"1.0","event_id":"sha256:4e14fec26f37affbe578af5f71356995a64d3e79bb4634dd791784743ee5b10c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JTJB3GT3A6HPTZD5DX3JLCGBWC/bundle.json","state_url":"https://pith.science/pith/JTJB3GT3A6HPTZD5DX3JLCGBWC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JTJB3GT3A6HPTZD5DX3JLCGBWC/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":"2026-05-29T18:53:03Z","links":{"resolver":"https://pith.science/pith/JTJB3GT3A6HPTZD5DX3JLCGBWC","bundle":"https://pith.science/pith/JTJB3GT3A6HPTZD5DX3JLCGBWC/bundle.json","state":"https://pith.science/pith/JTJB3GT3A6HPTZD5DX3JLCGBWC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JTJB3GT3A6HPTZD5DX3JLCGBWC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JTJB3GT3A6HPTZD5DX3JLCGBWC","merge_version":"pith-open-graph-merge-v1","event_count":8,"valid_event_count":8,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ab97311ec1655c98171109a048531957f657869ace788f6958ce6240c4cf4844","cross_cats_sorted":["cs.AI","cs.ET","cs.LG","cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-15T17:42:11Z","title_canon_sha256":"c51e08ec920687b178af2d86c507a6891e32361e26eaa8118b69bd21d05b08bf"},"schema_version":"1.0","source":{"id":"2605.16232","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16232","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16232v1","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16232","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"pith_short_12","alias_value":"JTJB3GT3A6HP","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"pith_short_16","alias_value":"JTJB3GT3A6HPTZD5","created_at":"2026-05-20T00:01:59Z"},{"alias_kind":"pith_short_8","alias_value":"JTJB3GT3","created_at":"2026-05-20T00:01:59Z"}],"graph_snapshots":[{"event_id":"sha256:ec005f86e212333ad7d4faa264dce63d00584d771810e7704e52d8c337c0a02d","target":"graph","created_at":"2026-05-20T00:01:59Z","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":[{"findings_count":0,"name":"shingle_duplication","ran_at":"2026-05-19T17:49:42.200380Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T17:49:41.809337Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:23.124922Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"external_links","ran_at":"2026-05-19T17:31:27.633037Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.622560Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T16:51:58.121564Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.16232/integrity.json","findings":[],"snapshot_sha256":"19b88cbbd8e6fae8db2af839e67de911bc00d16a1b41decc53fb1404af1716ac","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The accelerating convergence of smart metering, generative artificial intelligence, and quantum-inspired combinatorial optimisation is reshaping how energy utilities manage physical infrastructure, customer engagement, and environmental accountability","authors_text":"Pavan Manjunath, Thomas pruefer","cross_cats":["cs.AI","cs.ET","cs.LG","cs.SY","eess.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-15T17:42:11Z","title":"A Unified Generative-AI Framework for Smart Energy Infrastructure: Intelligent Gas Distribution, Utility Billing, Carbon Analytics, and Quantum-Inspired Optimisation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16232","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:ddc918dd694a16ab5990315171c568ed9349cd51b587b3f72d06c48e19e33f6a","target":"record","created_at":"2026-05-20T00:01:59Z","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":"ab97311ec1655c98171109a048531957f657869ace788f6958ce6240c4cf4844","cross_cats_sorted":["cs.AI","cs.ET","cs.LG","cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-15T17:42:11Z","title_canon_sha256":"c51e08ec920687b178af2d86c507a6891e32361e26eaa8118b69bd21d05b08bf"},"schema_version":"1.0","source":{"id":"2605.16232","kind":"arxiv","version":1}},"canonical_sha256":"4cd21d9a7b078ef9e47d1df69588c1b0aba72d8dd760313296dab3db0fbf32bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4cd21d9a7b078ef9e47d1df69588c1b0aba72d8dd760313296dab3db0fbf32bf","first_computed_at":"2026-05-20T00:01:59.219603Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:59.219603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KpeAi+KIDTESgSEjToQa4/U+QEdri3zfqEalZvS7yzGqNqyZqStZqyZQgD5aHTzRKUf7I5r2uXWPG5MvQnWcCg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:59.220438Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16232","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:3bea4601e62857338b1b8e560fe20bc254f76fe7c09b6d4eb5518a590f2c27e0","sha256:4e14fec26f37affbe578af5f71356995a64d3e79bb4634dd791784743ee5b10c","sha256:8793a9445c9490a8e4c686ffe5d1a056d117618e22d87135a6b29fcdb0675e2c","sha256:ecd066cdde4a70a2dbb8a639391786bcf787a8b942fa0f7d501c6517312fa762","sha256:fcb32965dd16a495c64da9bdd5b415ab33d1959e88db48cdc7d8b86a7ee9ee7c","sha256:fce8df970a52ff5a687a16587113466b2158b2683fe1d9518cad15dcf5924abd"]}],"invalid_events":[],"applied_event_ids":["sha256:ddc918dd694a16ab5990315171c568ed9349cd51b587b3f72d06c48e19e33f6a","sha256:ec005f86e212333ad7d4faa264dce63d00584d771810e7704e52d8c337c0a02d"],"state_sha256":"99b425b1b4f6ea09dcd5c66ef701e1186686e5d65394dea9c4e719cbe330ad9b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K+Yj/pTkXC/LsxbKHLA+yJyMhN+SnJJAtbwGleKbtnqnWvwkmX0XKXB5BgxUmIXYI3lrQkxLno1dtzHECZjgDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T18:53:03.107947Z","bundle_sha256":"f1f131a45bb587c10804828954bbc1b2d586c1a90ac4e5f1e1d849cc2269bcec"}}