{"id":"W3135507208","doi":"10.1016/j.jconhyd.2021.103793","title":"The correlation between drainage chemistry and weather for full-scale waste rock piles based on artificial neural network","year":2021,"lang":"en","type":"article","venue":"Journal of Contaminant Hydrology","topic":"Mine drainage and remediation techniques","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Artificial neural network; Drainage; Environmental science; Scale (ratio); Hydrology (agriculture); Geology; Geotechnical engineering; Computer science; Machine learning; Ecology; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002038749,0.0003430237,0.0002728462,0.0006265157,0.0001693452,0.0003125368,0.0003492766,0.0003731311,0.0005593096],"category_scores_gemma":[0.0008210959,0.000201963,0.0003404412,0.0005482106,0.0001593647,0.0004096128,0.0002005835,0.0002263202,0.0001115733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003192109,"about_ca_system_score_gemma":0.000286605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006043869,"about_ca_topic_score_gemma":0.008070065,"domain_scores_codex":[0.9999098,0.00001315971,0.000008627716,0.00002974753,0.00002409727,0.00001471958],"domain_scores_gemma":[0.9995477,0.0002205945,0.00006921451,0.00002712385,0.0001079398,0.00002746222],"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":[0.0008171118,0.0004843864,0.2015388,0.0001105813,0.0002350192,0.0002585092,0.0000614945,0.6973959,0.02058961,0.0001911804,0.0006099488,0.07770751],"study_design_scores_gemma":[0.000004376729,0.00002453069,0.02452286,0.00000167093,0.00001376155,0.00001288175,0.00001007068,0.9739623,0.001363087,0.00005286036,0.00002420221,0.000007275696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897826,0.00006308334,0.009458393,0.00002689134,0.00001543952,0.000006855012,0.0001476497,0.0001106362,0.0003885276],"genre_scores_gemma":[0.9984925,0.00002498562,0.001194823,0.000003134241,0.00000392518,0.000003464643,0.0001263522,0.000003425671,0.0001473901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006043869,"threshold_uncertainty_score":0.01201743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006974334734924817,"score_gpt":0.2227910448580212,"score_spread":0.2158167101230964,"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."}}