{"id":"W3201372231","doi":"10.3390/w13182485","title":"Machine Learning Models for Predicting Water Quality of Treated Fruit and Vegetable Wastewater","year":2021,"lang":"en","type":"article","venue":"Water","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Wastewater; Water quality; Chemical oxygen demand; Sewage treatment; Filtration (mathematics); Environmental science; Biochemical oxygen demand; Linear regression; Total suspended solids; Pulp and paper industry; Predictive modelling; Process engineering; Environmental engineering; Mathematics; Computer science; Machine learning; Engineering; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005793814,0.0001297141,0.0002590399,0.00002354349,0.0001526683,0.00004419213,0.00007845516,0.00006180637,0.0003588818],"category_scores_gemma":[0.00001592494,0.00007507853,0.00009017047,0.00004310551,0.00006281965,0.000212748,0.0002037375,0.00008991657,0.00003170909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003113672,"about_ca_system_score_gemma":0.00000174891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001535466,"about_ca_topic_score_gemma":0.00002426236,"domain_scores_codex":[0.9986902,0.0001354339,0.0003220034,0.0003241475,0.0001885625,0.0003396603],"domain_scores_gemma":[0.9996546,0.00003488499,0.00003660173,0.0001876425,0.00001977957,0.00006648431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002686831,0.00004564953,0.1098582,0.00006791452,0.00005430706,0.000002721685,0.002805416,0.00968987,0.8772424,0.000009081608,0.00001333726,0.0001841242],"study_design_scores_gemma":[0.0004122564,0.0000330557,0.001270069,0.00001469458,0.00004891357,0.0000023753,0.0001223449,0.01156326,0.9844639,0.001158904,0.0007700006,0.0001402056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986494,0.0000399472,0.0003339218,0.0002417184,0.00004308532,0.00007331949,0.00001177945,0.0000348664,0.0005719521],"genre_scores_gemma":[0.9895067,0.000007754466,0.0006895105,0.00001616361,0.00004115675,0.00001403287,0.00009128353,0.00001654371,0.009616891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1085882,"threshold_uncertainty_score":0.3929504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04407425651852554,"score_gpt":0.2586569318111172,"score_spread":0.2145826752925916,"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."}}