{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:654WUI62BSRLO5XOZS7ZXKNT64","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":"d94853fd06acc97b8175a64e707eb21414e4b29ca6c6b7cf43f8facfc09ee569","cross_cats_sorted":["stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2025-01-14T02:03:33Z","title_canon_sha256":"5b6c6d25ca977f8a0aab647a3a22f52b181911e2bca8091b1f6b18ea2a4efd78"},"schema_version":"1.0","source":{"id":"2501.17865","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.17865","created_at":"2026-07-05T10:07:03Z"},{"alias_kind":"arxiv_version","alias_value":"2501.17865v1","created_at":"2026-07-05T10:07:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17865","created_at":"2026-07-05T10:07:03Z"},{"alias_kind":"pith_short_12","alias_value":"654WUI62BSRL","created_at":"2026-07-05T10:07:03Z"},{"alias_kind":"pith_short_16","alias_value":"654WUI62BSRLO5XO","created_at":"2026-07-05T10:07:03Z"},{"alias_kind":"pith_short_8","alias_value":"654WUI62","created_at":"2026-07-05T10:07:03Z"}],"graph_snapshots":[{"event_id":"sha256:a27529a506e30e304a29d29f86f4d7c8e15534fe37624131aeef6b1250ee36df","target":"graph","created_at":"2026-07-05T10:07:03Z","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/2501.17865/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper addresses the environmental impacts linked to hazardous emissions from gas turbines, with a specific focus on employing various machine learning (ML) models to predict the emissions of Carbon Monoxide (CO) and Nitrogen Oxides (NOx) as part of a Predictive Emission Monitoring System (PEMS). We employ a comprehensive approach using multiple predictive models to offer insights on enhancing regulatory compliance and optimizing operational parameters to reduce environmental effects effectively. Our investigation explores a range of machine learning models including linear models, ensembl","authors_text":"David Ghelardi, Kamyar Zeinalipour, Laure Barriere, Marco Gori","cross_cats":["stat.AP"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2025-01-14T02:03:33Z","title":"Application of Machine Learning Models for Carbon Monoxide and Nitrogen Oxides Emission Prediction in Gas Turbines"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17865","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":"sha256:495064a513b2a70d2aa629355dc0e177343631a105ea752d01187c661acd5cfc","target":"record","created_at":"2026-07-05T10:07:03Z","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":"d94853fd06acc97b8175a64e707eb21414e4b29ca6c6b7cf43f8facfc09ee569","cross_cats_sorted":["stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2025-01-14T02:03:33Z","title_canon_sha256":"5b6c6d25ca977f8a0aab647a3a22f52b181911e2bca8091b1f6b18ea2a4efd78"},"schema_version":"1.0","source":{"id":"2501.17865","kind":"arxiv","version":1}},"canonical_sha256":"f7796a23da0ca2b776eeccbf9ba9b3f725465f9d26aa5e7f3002c09385cbf0ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f7796a23da0ca2b776eeccbf9ba9b3f725465f9d26aa5e7f3002c09385cbf0ed","first_computed_at":"2026-07-05T10:07:03.074649Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:07:03.074649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6RV4D6Q5inS/nM/6bOf0VBsgXVdkLSzbsB0+hkSp1rwyUju/cPZllUfd40WuI2vX1sBnkgfp+nBwa8UKKG48AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:07:03.075175Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.17865","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:495064a513b2a70d2aa629355dc0e177343631a105ea752d01187c661acd5cfc","sha256:a27529a506e30e304a29d29f86f4d7c8e15534fe37624131aeef6b1250ee36df"],"state_sha256":"a2b1cd087ed1e9611aad3fc6cf81636f88a2edd5d25da256db5a65d020446bf3"}