{"id":"W4400886706","doi":"10.1016/j.jenvman.2024.121932","title":"An ensemble model for accurate prediction of key water quality parameters in river based on deep learning methods","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Key (lock); Water quality; Ensemble learning; Computer science; Artificial intelligence; Quality (philosophy); Machine learning; Environmental 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":[0.0006849673,0.0006766178,0.001060024,0.0004978233,0.0003681947,0.0005623056,0.0009482292,0.000941598,0.0008917854],"category_scores_gemma":[0.001126962,0.000433511,0.0007361416,0.0005546451,0.0002484744,0.0009406722,0.0007350984,0.001300929,0.0002095004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004948261,"about_ca_system_score_gemma":0.0008340942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0182188,"about_ca_topic_score_gemma":0.01525016,"domain_scores_codex":[0.9998074,0.00003814353,0.00001388215,0.00006005801,0.00003993476,0.00004068475],"domain_scores_gemma":[0.9995729,0.0001851898,0.00003221954,0.00003714896,0.0001477593,0.00002474722],"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":[0.00005216911,0.00006069617,0.001592658,0.00001666517,0.0000628108,0.00002892778,0.00001659007,0.9473069,0.001422912,0.0007269916,0.0007290738,0.0479836],"study_design_scores_gemma":[5.397464e-7,0.000003003343,0.00006368847,5.19218e-7,0.000002289561,8.794224e-7,4.87756e-7,0.9997366,0.00007106556,0.0001028211,0.00001736177,8.298039e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2219143,0.001022484,0.7733107,0.0002992279,0.0001771639,0.00003221739,0.0002997413,0.0008697207,0.002074515],"genre_scores_gemma":[0.9619431,0.0002864299,0.03483261,0.00009245305,0.00005720577,0.00006032121,0.000436966,0.00003761609,0.002253173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0182188,"threshold_uncertainty_score":0.0362255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05465980488204389,"score_gpt":0.3316260320435537,"score_spread":0.2769662271615099,"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."}}