{"id":"W2051708725","doi":"10.1021/acs.energyfuels.5b00528","title":"Improved Density Prediction for Mixtures of Native and Refined Heavy Oil with Solvents","year":2015,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Phase Equilibria and Thermodynamics","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Schlumberger (Canada); University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Virtual Materials Group; Petrobras; China National Offshore Oil Corporation; Shell","keywords":"Toluene; Chemistry; Asphaltene; Naphtha; Hydrocarbon; Distillation; Fraction (chemistry); Heptane; Hydrocarbon mixtures; Mixing (physics); Diesel fuel; Benzene; Volume (thermodynamics); Analytical Chemistry (journal); Mass fraction; Thermodynamics; Chromatography; Organic chemistry","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":[0.00005795707,0.00008839028,0.0001276513,0.00003356548,0.00001737888,0.000006703727,0.00003967259,0.00005170855,9.824558e-7],"category_scores_gemma":[0.00001462301,0.00007515868,0.00002070576,0.0000494168,0.00002662986,0.00008520963,0.00001272974,0.00003431461,9.084377e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002931496,"about_ca_system_score_gemma":0.0000189169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003910961,"about_ca_topic_score_gemma":0.00008477981,"domain_scores_codex":[0.9996184,0.000009078966,0.00009695285,0.00009700907,0.00006966624,0.0001089642],"domain_scores_gemma":[0.9997298,0.0000233178,0.00002784584,0.00009459295,0.00006153562,0.00006289419],"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":[0.0008740835,0.00008657583,0.0003288471,0.0002083584,0.000379569,0.00000430264,0.0007687394,0.002770816,0.9722844,0.004050726,0.0004151346,0.0178285],"study_design_scores_gemma":[0.003045489,0.0005688684,0.0005216045,0.00006732762,0.00006999549,0.00001247651,0.00006200807,0.1367607,0.8512471,0.004901166,0.002495662,0.000247688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790156,0.0002893115,0.01901226,0.00001522249,0.0001751317,0.00003245944,0.00003985534,0.00008038764,0.001339741],"genre_scores_gemma":[0.9987937,0.00002819291,0.0008407878,0.00001469472,0.00006602608,0.00001671672,0.00002934015,0.00001946837,0.0001910459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1339898,"threshold_uncertainty_score":0.3064882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01011827561596069,"score_gpt":0.2071997588072522,"score_spread":0.1970814831912915,"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."}}