{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01170168,0.0009476831,0.001426187,0.002358526,0.000814612,0.003885132,0.002541836,0.003108804,0.01119209],"category_scores_gemma":[0.02643349,0.0008754143,0.001160942,0.002568176,0.005451606,0.003896395,0.002199186,0.003940139,0.002942682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002703185,"about_ca_system_score_gemma":0.00281609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001903068,"about_ca_topic_score_gemma":0.001404216,"domain_scores_codex":[0.9897823,0.005455324,0.0003896642,0.0009468778,0.003263787,0.0001621605],"domain_scores_gemma":[0.9785597,0.01535556,0.001009182,0.002623196,0.002257413,0.0001950539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008734336,0.0001954309,0.001790954,0.003439927,0.0002094463,0.0001332825,0.0001883762,0.05335772,0.00426215,0.691651,0.01535303,0.2293313],"study_design_scores_gemma":[0.00006903656,0.0002265952,0.001587205,0.002261106,0.00009459129,0.0002488373,0.000174634,0.1237744,0.009523218,0.6524227,0.2094445,0.0001733133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003345131,0.03600942,0.9218163,0.004961584,0.001975484,0.0004369304,0.0003390554,0.0007501635,0.03036601],"genre_scores_gemma":[0.1409125,0.05936141,0.774033,0.003667126,0.002387888,0.002512615,0.000649486,0.0006241636,0.01585177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01170168,"threshold_uncertainty_score":0.06188512,"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."}}