{"id":"W4406362399","doi":"10.1016/j.rineng.2025.104035","title":"Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Phase Equilibria and Thermodynamics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Compressibility factor; Compressibility; Equation of state; State (computer science); Carbon dioxide; Decision tree; Computer science; Tree (set theory); Data mining; Thermodynamics; Mathematics; Algorithm; Chemistry; Physics; Mathematical analysis; Organic chemistry","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":[0.0001209438,0.0001376157,0.0002371113,0.0002246129,0.000009763358,0.000009181009,0.00006193681,0.0000619812,4.477032e-7],"category_scores_gemma":[0.0001208203,0.0001455082,0.00002595036,0.0002229645,0.00003257424,0.00008798252,0.00003514948,0.0001185595,2.303746e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007927576,"about_ca_system_score_gemma":0.00002479624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005672588,"about_ca_topic_score_gemma":0.00002026088,"domain_scores_codex":[0.9991606,0.00001496793,0.0004405004,0.0001547677,0.00008426705,0.000144841],"domain_scores_gemma":[0.9995278,0.0001854313,0.00004936862,0.0001679006,0.00003076484,0.00003871924],"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":[0.00004703171,0.00002082887,0.001527196,0.0005448529,0.00004328197,0.000001151243,0.0003521258,0.647852,0.3482051,0.0000846556,1.19139e-7,0.00132164],"study_design_scores_gemma":[0.0005179662,0.00001442831,0.006546296,0.0003385516,0.0000129245,2.532198e-7,0.0000183418,0.8018708,0.1905499,0.00004899987,0.0000074575,0.00007402134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8978821,0.0004979753,0.1009777,0.000005369645,0.0001283798,0.0001562785,0.0001713361,0.00006889627,0.0001119326],"genre_scores_gemma":[0.998949,0.00008441099,0.0009236801,7.531193e-7,0.000008353363,0.000004621027,0.0000117157,0.00001493168,0.000002541795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1576551,"threshold_uncertainty_score":0.5933653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02944034121928963,"score_gpt":0.2530067987905343,"score_spread":0.2235664575712447,"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."}}