{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:LORJ342TLAZKVSV44UFNF5BYER","short_pith_number":"pith:LORJ342T","schema_version":"1.0","canonical_sha256":"5ba29df3535832aacabce50ad2f43824654a7c3a70277f69517dcbbd7a9c021e","source":{"kind":"arxiv","id":"2110.09360","version":1},"attestation_state":"computed","paper":{"title":"Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"\\'Agatha P. F. Lima, Cheng Chen, Daniel Mira, Fernando A. Rochinha, Rodolfo S. M. Freitas, Xi Jiang","submitted_at":"2021-10-18T14:43:50Z","abstract_excerpt":"Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels. The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. Here, Gaussian Process (GP) and probabilistic generative models are adopted. GP is a popular non-parametric Bayesian approach to b"},"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":"2110.09360","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-10-18T14:43:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"52be6ec29411836d7620413287052efb493139bd1875c52fe90c34ceeed73fc0","abstract_canon_sha256":"6fc3089303c4be50aad91ee99ad1418b91e8461b0277eb0a2e72732b3845bf51"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:23:27.645187Z","signature_b64":"uFLBcBKfBtj9IEjIELWhWCcG/gyM21oLBmLXtNhYtMGWGmZpXvKtz1fuGfPwVrASLB8nIRIej24T8UK4AXJ8Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ba29df3535832aacabce50ad2f43824654a7c3a70277f69517dcbbd7a9c021e","last_reissued_at":"2026-07-05T03:23:27.644741Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:23:27.644741Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"\\'Agatha P. F. Lima, Cheng Chen, Daniel Mira, Fernando A. Rochinha, Rodolfo S. M. Freitas, Xi Jiang","submitted_at":"2021-10-18T14:43:50Z","abstract_excerpt":"Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels. The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. Here, Gaussian Process (GP) and probabilistic generative models are adopted. GP is a popular non-parametric Bayesian approach to b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.09360","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/2110.09360/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":"2110.09360","created_at":"2026-07-05T03:23:27.644796+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.09360v1","created_at":"2026-07-05T03:23:27.644796+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.09360","created_at":"2026-07-05T03:23:27.644796+00:00"},{"alias_kind":"pith_short_12","alias_value":"LORJ342TLAZK","created_at":"2026-07-05T03:23:27.644796+00:00"},{"alias_kind":"pith_short_16","alias_value":"LORJ342TLAZKVSV4","created_at":"2026-07-05T03:23:27.644796+00:00"},{"alias_kind":"pith_short_8","alias_value":"LORJ342T","created_at":"2026-07-05T03:23:27.644796+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/LORJ342TLAZKVSV44UFNF5BYER","json":"https://pith.science/pith/LORJ342TLAZKVSV44UFNF5BYER.json","graph_json":"https://pith.science/api/pith-number/LORJ342TLAZKVSV44UFNF5BYER/graph.json","events_json":"https://pith.science/api/pith-number/LORJ342TLAZKVSV44UFNF5BYER/events.json","paper":"https://pith.science/paper/LORJ342T"},"agent_actions":{"view_html":"https://pith.science/pith/LORJ342TLAZKVSV44UFNF5BYER","download_json":"https://pith.science/pith/LORJ342TLAZKVSV44UFNF5BYER.json","view_paper":"https://pith.science/paper/LORJ342T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.09360&json=true","fetch_graph":"https://pith.science/api/pith-number/LORJ342TLAZKVSV44UFNF5BYER/graph.json","fetch_events":"https://pith.science/api/pith-number/LORJ342TLAZKVSV44UFNF5BYER/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LORJ342TLAZKVSV44UFNF5BYER/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LORJ342TLAZKVSV44UFNF5BYER/action/storage_attestation","attest_author":"https://pith.science/pith/LORJ342TLAZKVSV44UFNF5BYER/action/author_attestation","sign_citation":"https://pith.science/pith/LORJ342TLAZKVSV44UFNF5BYER/action/citation_signature","submit_replication":"https://pith.science/pith/LORJ342TLAZKVSV44UFNF5BYER/action/replication_record"}},"created_at":"2026-07-05T03:23:27.644796+00:00","updated_at":"2026-07-05T03:23:27.644796+00:00"}