{"id":"W4400098007","doi":"10.1002/cjce.25374","title":"Experimental methods in chemical engineering: Monte Carlo","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université de Sherbrooke","funders":"","keywords":"Monte Carlo method; Computer science; Markov chain Monte Carlo; Frequentist inference; Sampling (signal processing); Range (aeronautics); Uncertainty quantification; Bayesian inference; Mathematical optimization; Bayesian probability; Machine learning; Engineering; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00146994,0.0001885446,0.0002771521,0.0002207973,0.00003311384,0.0002874697,0.0007008816,0.00009424269,0.0002812681],"category_scores_gemma":[0.0006400502,0.0001433764,0.00009802874,0.0003224304,0.00009153273,0.0002115572,0.00005176737,0.000564248,0.00002144159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004353667,"about_ca_system_score_gemma":0.0003257149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070297,"about_ca_topic_score_gemma":0.00001399694,"domain_scores_codex":[0.9985535,0.00004858642,0.0004627832,0.000193885,0.0002594408,0.0004818131],"domain_scores_gemma":[0.9990824,0.0002395442,0.00006015372,0.0001903909,0.00003839305,0.0003891261],"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":[0.000004000001,0.000003442856,0.00001014955,0.00003045907,0.000005242493,0.0001051163,0.0005134577,0.06862007,0.9300929,0.0003970414,0.0001003229,0.0001178308],"study_design_scores_gemma":[0.00009506787,0.00001548982,0.0000208229,0.0001615738,0.000007888502,0.0003185388,0.00001065991,0.1376759,0.8600047,0.00002975711,0.001508221,0.0001514047],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935062,0.001883714,0.002321723,0.0005413123,0.001557273,0.00006925,0.000004289171,0.00005066002,0.00006558521],"genre_scores_gemma":[0.9668739,5.897511e-7,0.03267841,0.0000416867,0.0003480589,0.000005804479,3.097402e-7,0.00003307395,0.00001811506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07008822,"threshold_uncertainty_score":0.5846719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118048925458462,"score_gpt":0.2792093896760455,"score_spread":0.2674044971301993,"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."}}