{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CCQCWCF5G5ACDFHIEUX5XLZLJK","short_pith_number":"pith:CCQCWCF5","schema_version":"1.0","canonical_sha256":"10a02b08bd37402194e8252fdbaf2b4a9924fbd71f117667068675136a57b8ef","source":{"kind":"arxiv","id":"2408.01298","version":2},"attestation_state":"computed","paper":{"title":"Probabilistic Inversion Modeling of Gas Emissions: A Gradient-Based MCMC Estimation of Gaussian Plume Parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.ME"],"primary_cat":"stat.AP","authors_text":"Christopher Nemeth, Matthew Jones, Philip Jonathan, Thomas Newman","submitted_at":"2024-08-02T14:41:03Z","abstract_excerpt":"In response to global concerns regarding air quality and the environmental impact of greenhouse gas emissions, detecting and quantifying sources of emissions has become critical. To understand this impact and target mitigations effectively, methods for accurate quantification of greenhouse gas emissions are required. In this paper, we focus on the inversion of concentration measurements to estimate source location and emission rate. In practice, such methods often rely on atmospheric stability class-based Gaussian plume dispersion models. However, incorrectly identifying the atmospheric stabil"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2408.01298","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2024-08-02T14:41:03Z","cross_cats_sorted":["stat.CO","stat.ME"],"title_canon_sha256":"2fd0b3bf36048d9a860a9345353665bf366d82900d8fe1a76e8ea83f133267bf","abstract_canon_sha256":"cba3e22cbfd8243b0ad9894d7438630e0986badcc2ac57d44fb8d9c388d2d448"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:19:11.658406Z","signature_b64":"xPzj6ErNlpJyFZj0XJLSL3uqPV1oayDNrPd8U9/65Zk4Y8GS85LaJe4fEW07cMENAjFHxd/cjR8z/0UsPCdkCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10a02b08bd37402194e8252fdbaf2b4a9924fbd71f117667068675136a57b8ef","last_reissued_at":"2026-07-05T09:19:11.657854Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:19:11.657854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Inversion Modeling of Gas Emissions: A Gradient-Based MCMC Estimation of Gaussian Plume Parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.ME"],"primary_cat":"stat.AP","authors_text":"Christopher Nemeth, Matthew Jones, Philip Jonathan, Thomas Newman","submitted_at":"2024-08-02T14:41:03Z","abstract_excerpt":"In response to global concerns regarding air quality and the environmental impact of greenhouse gas emissions, detecting and quantifying sources of emissions has become critical. To understand this impact and target mitigations effectively, methods for accurate quantification of greenhouse gas emissions are required. In this paper, we focus on the inversion of concentration measurements to estimate source location and emission rate. In practice, such methods often rely on atmospheric stability class-based Gaussian plume dispersion models. However, incorrectly identifying the atmospheric stabil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.01298","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/2408.01298/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2408.01298","created_at":"2026-07-05T09:19:11.657913+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.01298v2","created_at":"2026-07-05T09:19:11.657913+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.01298","created_at":"2026-07-05T09:19:11.657913+00:00"},{"alias_kind":"pith_short_12","alias_value":"CCQCWCF5G5AC","created_at":"2026-07-05T09:19:11.657913+00:00"},{"alias_kind":"pith_short_16","alias_value":"CCQCWCF5G5ACDFHI","created_at":"2026-07-05T09:19:11.657913+00:00"},{"alias_kind":"pith_short_8","alias_value":"CCQCWCF5","created_at":"2026-07-05T09:19:11.657913+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.14597","citing_title":"Deep Learning Surrogates for Real-Time Gas Emission Inversion","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CCQCWCF5G5ACDFHIEUX5XLZLJK","json":"https://pith.science/pith/CCQCWCF5G5ACDFHIEUX5XLZLJK.json","graph_json":"https://pith.science/api/pith-number/CCQCWCF5G5ACDFHIEUX5XLZLJK/graph.json","events_json":"https://pith.science/api/pith-number/CCQCWCF5G5ACDFHIEUX5XLZLJK/events.json","paper":"https://pith.science/paper/CCQCWCF5"},"agent_actions":{"view_html":"https://pith.science/pith/CCQCWCF5G5ACDFHIEUX5XLZLJK","download_json":"https://pith.science/pith/CCQCWCF5G5ACDFHIEUX5XLZLJK.json","view_paper":"https://pith.science/paper/CCQCWCF5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.01298&json=true","fetch_graph":"https://pith.science/api/pith-number/CCQCWCF5G5ACDFHIEUX5XLZLJK/graph.json","fetch_events":"https://pith.science/api/pith-number/CCQCWCF5G5ACDFHIEUX5XLZLJK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CCQCWCF5G5ACDFHIEUX5XLZLJK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CCQCWCF5G5ACDFHIEUX5XLZLJK/action/storage_attestation","attest_author":"https://pith.science/pith/CCQCWCF5G5ACDFHIEUX5XLZLJK/action/author_attestation","sign_citation":"https://pith.science/pith/CCQCWCF5G5ACDFHIEUX5XLZLJK/action/citation_signature","submit_replication":"https://pith.science/pith/CCQCWCF5G5ACDFHIEUX5XLZLJK/action/replication_record"}},"created_at":"2026-07-05T09:19:11.657913+00:00","updated_at":"2026-07-05T09:19:11.657913+00:00"}