{"id":"W2322367949","doi":"10.1021/ef5018169","title":"Deriving the Molecular Composition of Middle Distillates by Integrating Statistical Modeling with Advanced Hydrocarbon Characterization","year":2014,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Distillation; Chemistry; Hydrocarbon mixtures; Hydrocarbon; Flame ionization detector; Monte Carlo method; Gas chromatography; Characterization (materials science); Biological system; Chromatography; Materials science; Organic chemistry; Nanotechnology; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000679223,0.000122639,0.0001741129,0.00002201749,0.00010445,0.00004041737,0.0001044376,0.0000435966,0.00001679846],"category_scores_gemma":[0.00003322986,0.00008539233,0.00003381881,0.00009259649,0.00004508829,0.00007151541,0.0000191685,0.00008459897,3.238345e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001953757,"about_ca_system_score_gemma":0.00001203764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006007401,"about_ca_topic_score_gemma":0.000007967464,"domain_scores_codex":[0.9992604,0.00003110028,0.0002074645,0.0001872503,0.000172178,0.0001416223],"domain_scores_gemma":[0.9995537,0.0000603669,0.0001408257,0.0001558698,0.0000525835,0.0000365886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001540158,0.00002055154,0.0001077743,0.00003926597,0.00003024535,6.510315e-7,0.00007547512,0.004519737,0.9754493,0.001337727,3.048063e-7,0.01840355],"study_design_scores_gemma":[0.0001337289,0.00001802486,0.00000689,0.0001843282,0.00004777545,0.000001939753,0.00006932409,0.3845645,0.6146134,0.0001693545,0.00008840664,0.0001023644],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5655044,0.000084548,0.4331475,0.00002858371,0.000004823671,0.000001904524,0.000007933068,0.00002367921,0.00119665],"genre_scores_gemma":[0.9979476,0.0000141326,0.001488466,0.00004848595,0.00003032902,0.000009029352,0.0003445972,0.00001950153,0.00009789697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4324431,"threshold_uncertainty_score":0.3482198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004610597258802213,"score_gpt":0.1974753837377397,"score_spread":0.1928647864789375,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}