{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QZDTRSS7RDTVDQU6ZEXUE3C4YZ","short_pith_number":"pith:QZDTRSS7","schema_version":"1.0","canonical_sha256":"864738ca5f88e751c29ec92f426c5cc66fb3a98f8bd0f1b1b13a236cb87da90c","source":{"kind":"arxiv","id":"2606.17413","version":1},"attestation_state":"computed","paper":{"title":"Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.LG","authors_text":"Alejandro Calle-Saldarriaga, Felix Jimenez, Jack Grosskreuz, Jiazheng Wang, Jonathan Hobbs, Matthias Katzfuss","submitted_at":"2026-06-16T01:49:50Z","abstract_excerpt":"Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget. NASA's Orbiting Carbon Observatory-2 (OCO-2) estimates column-averaged dry-air mole fractions of CO2 (XCO2) using high-resolution spectra. However, current operational retrieval algorithms are computationally expensive and do not properly quantify uncertainties. We present a novel deep learning framework that addresses these challenges. Due to the difficulties of ground-truth data for real satellite observations, we develop and validate our approach using a high-fidelity simulatio"},"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":"2606.17413","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-16T01:49:50Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"07dd6d01e364f7e24ea84ff76aeca78f30f394c64b3ab59acad8ea25cda4e4a4","abstract_canon_sha256":"deda71db8ed470f6b47a4a5ff8b4fa947d4f0ac8a134e710aa1e14d483ffba95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:12.204531Z","signature_b64":"OD+Cjc8K7aF00pEdWOShOFY1598MmGoIJbPV0fe1OuosxG9VpSBVPCo7eRwbl9i9yroxb5j4H8xW5ay1h9NvAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"864738ca5f88e751c29ec92f426c5cc66fb3a98f8bd0f1b1b13a236cb87da90c","last_reissued_at":"2026-06-19T16:10:12.204171Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:12.204171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.LG","authors_text":"Alejandro Calle-Saldarriaga, Felix Jimenez, Jack Grosskreuz, Jiazheng Wang, Jonathan Hobbs, Matthias Katzfuss","submitted_at":"2026-06-16T01:49:50Z","abstract_excerpt":"Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget. NASA's Orbiting Carbon Observatory-2 (OCO-2) estimates column-averaged dry-air mole fractions of CO2 (XCO2) using high-resolution spectra. However, current operational retrieval algorithms are computationally expensive and do not properly quantify uncertainties. We present a novel deep learning framework that addresses these challenges. Due to the difficulties of ground-truth data for real satellite observations, we develop and validate our approach using a high-fidelity simulatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.17413","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/2606.17413/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":"2606.17413","created_at":"2026-06-19T16:10:12.204232+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.17413v1","created_at":"2026-06-19T16:10:12.204232+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.17413","created_at":"2026-06-19T16:10:12.204232+00:00"},{"alias_kind":"pith_short_12","alias_value":"QZDTRSS7RDTV","created_at":"2026-06-19T16:10:12.204232+00:00"},{"alias_kind":"pith_short_16","alias_value":"QZDTRSS7RDTVDQU6","created_at":"2026-06-19T16:10:12.204232+00:00"},{"alias_kind":"pith_short_8","alias_value":"QZDTRSS7","created_at":"2026-06-19T16:10:12.204232+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QZDTRSS7RDTVDQU6ZEXUE3C4YZ","json":"https://pith.science/pith/QZDTRSS7RDTVDQU6ZEXUE3C4YZ.json","graph_json":"https://pith.science/api/pith-number/QZDTRSS7RDTVDQU6ZEXUE3C4YZ/graph.json","events_json":"https://pith.science/api/pith-number/QZDTRSS7RDTVDQU6ZEXUE3C4YZ/events.json","paper":"https://pith.science/paper/QZDTRSS7"},"agent_actions":{"view_html":"https://pith.science/pith/QZDTRSS7RDTVDQU6ZEXUE3C4YZ","download_json":"https://pith.science/pith/QZDTRSS7RDTVDQU6ZEXUE3C4YZ.json","view_paper":"https://pith.science/paper/QZDTRSS7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.17413&json=true","fetch_graph":"https://pith.science/api/pith-number/QZDTRSS7RDTVDQU6ZEXUE3C4YZ/graph.json","fetch_events":"https://pith.science/api/pith-number/QZDTRSS7RDTVDQU6ZEXUE3C4YZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QZDTRSS7RDTVDQU6ZEXUE3C4YZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QZDTRSS7RDTVDQU6ZEXUE3C4YZ/action/storage_attestation","attest_author":"https://pith.science/pith/QZDTRSS7RDTVDQU6ZEXUE3C4YZ/action/author_attestation","sign_citation":"https://pith.science/pith/QZDTRSS7RDTVDQU6ZEXUE3C4YZ/action/citation_signature","submit_replication":"https://pith.science/pith/QZDTRSS7RDTVDQU6ZEXUE3C4YZ/action/replication_record"}},"created_at":"2026-06-19T16:10:12.204232+00:00","updated_at":"2026-06-19T16:10:12.204232+00:00"}