{"id":"W7116661436","doi":"10.1021/acs.jcim.5c02281","title":"Data-Driven Approach for Predicting Gasoline Yield in an FCC Unit Charged with Light and Heavy Feedstocks: A PCA-Guided Grouping for Enhanced Modeling Experience","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Heat transfer and supercritical fluids","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"King Fahd University of Petroleum and Minerals","keywords":"Cluster analysis; Linear regression; Regression analysis; Kriging; Regression; Partial least squares regression; Gasoline; Data set; Robust regression; Mixture model","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003235289,0.0001289501,0.0002716153,0.0001516472,0.00006221898,0.00008959191,0.0001336202,0.00009667725,6.443369e-7],"category_scores_gemma":[0.0001326517,0.0001089087,0.00002928265,0.0001050459,0.00001267844,0.001730134,0.00002251096,0.000216631,2.376404e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002894558,"about_ca_system_score_gemma":0.00003758682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004417001,"about_ca_topic_score_gemma":0.00000193731,"domain_scores_codex":[0.9988595,0.000006403146,0.0007014056,0.0001022423,0.0001176498,0.0002128007],"domain_scores_gemma":[0.9995102,0.00006792943,0.000009403305,0.0001025964,0.0001914113,0.0001185088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005758987,0.0000534982,0.00007674927,0.001190262,0.00005621688,4.132227e-7,0.008204935,0.7535164,0.2330815,0.0006495703,0.00001917148,0.002575349],"study_design_scores_gemma":[0.001170767,0.00005328703,6.675497e-7,0.0003487361,0.00002555625,0.00001199001,0.001465097,0.9670564,0.02964914,0.0000908501,0.000010652,0.0001168804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4723037,0.00007358893,0.527303,0.00005977015,0.00002021463,0.0001503038,0.000008097472,0.00001743632,0.0000639424],"genre_scores_gemma":[0.970614,0.00007404168,0.02902796,0.0001493879,0.00005735188,0.00002342847,0.0000438507,0.000009645554,3.3345e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4983103,"threshold_uncertainty_score":0.4441167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06172449066086849,"score_gpt":0.299763762513618,"score_spread":0.2380392718527495,"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."}}