{"id":"W2039463669","doi":"10.1016/j.scitotenv.2014.04.125","title":"Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: A probability bounds approach","year":2014,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Mine drainage and remediation techniques","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kelowna General Hospital; University of British Columbia, Okanagan Campus; University of British Columbia Hospital","funders":"","keywords":"Support vector machine; Drainage; Artificial neural network; Machine learning; Artificial intelligence; Identification (biology); Computer science; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00561875,0.001418869,0.001773278,0.002054404,0.0005473241,0.002777934,0.001654078,0.001602376,0.0009369115],"category_scores_gemma":[0.01710032,0.001076085,0.00170234,0.001727699,0.001185143,0.003589932,0.002334335,0.002222668,0.0001741161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00125086,"about_ca_system_score_gemma":0.001339985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006457482,"about_ca_topic_score_gemma":0.003289119,"domain_scores_codex":[0.9977671,0.0008328029,0.0001631961,0.0003091275,0.000796607,0.0001312429],"domain_scores_gemma":[0.9892546,0.008711203,0.0006266165,0.0004791618,0.0008218843,0.0001065416],"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":[0.0000381865,0.00003738592,0.0004805266,0.00003947449,0.00008988829,0.00001601269,0.00002502154,0.9561282,0.0006007602,0.01097039,0.0001654182,0.03140883],"study_design_scores_gemma":[0.000001524278,0.000007622496,0.00009061409,0.000004398946,0.000007808462,0.000003221531,0.000001813328,0.9936441,0.0002770394,0.005883968,0.00007258383,0.000005158052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00817471,0.0003859429,0.9906232,0.0001134004,0.0000128016,0.00001435405,0.00002932127,0.00008795076,0.0005583353],"genre_scores_gemma":[0.7003288,0.00113509,0.2960873,0.0001260231,0.0001846949,0.0002095852,0.0002776282,0.0001193291,0.001531557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006457482,"threshold_uncertainty_score":0.02971518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01054264475862448,"score_gpt":0.2187327236387387,"score_spread":0.2081900788801142,"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."}}