{"id":"W3016987998","doi":"10.1007/s11270-020-04541-x","title":"Artificial Neural Network for Prediction of Full-Scale Seepage Flow Rate at the Equity Silver Mine","year":2020,"lang":"en","type":"article","venue":"Water Air & Soil Pollution","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Artificial neural network; Hydrogeology; Drainage; Environmental science; Volumetric flow rate; Field (mathematics); Scale (ratio); Machine learning; Hydrology (agriculture); Meteorology; Engineering; Computer science; Geotechnical engineering; Mathematics; Geography","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":[0.000373768,0.0005662296,0.0005007818,0.0004892752,0.0003064768,0.0004167233,0.0005274533,0.0009371347,0.001057058],"category_scores_gemma":[0.00103116,0.0002970585,0.0003986696,0.0003888032,0.0001811257,0.0003867189,0.000264117,0.0006396336,0.0001765797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006102735,"about_ca_system_score_gemma":0.0005530181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03631818,"about_ca_topic_score_gemma":0.02041159,"domain_scores_codex":[0.99992,0.00001785734,0.000005845424,0.00002709904,0.00001360191,0.00001555838],"domain_scores_gemma":[0.9995862,0.0002555377,0.00003049295,0.0000147677,0.00008235287,0.00003064644],"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":[0.0002053439,0.0002172931,0.01899045,0.00002754542,0.00004949133,0.00007941722,0.00001983608,0.9617168,0.001284984,0.0001420909,0.0005359596,0.01673075],"study_design_scores_gemma":[0.000002778075,0.000008191861,0.001668262,7.917815e-7,0.000001606285,0.000001175963,0.000003886032,0.9981711,0.00009841091,0.00003089854,0.00001146746,0.000001453312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899358,0.0001183876,0.008202841,0.0001075269,0.00004452918,0.00001464959,0.0003049321,0.0001923915,0.001078998],"genre_scores_gemma":[0.9978592,0.00002928693,0.001298744,0.000006954916,0.000006493994,0.00001003428,0.0001609215,0.000004139526,0.0006241783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03631818,"threshold_uncertainty_score":0.07221359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0226026130383249,"score_gpt":0.2242553473980189,"score_spread":0.201652734359694,"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."}}