{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001873266,0.001009354,0.0006981525,0.0006761933,0.0002915774,0.0008268294,0.0008464286,0.001264073,0.00107008],"category_scores_gemma":[0.003959018,0.0003470727,0.0007983266,0.0005830542,0.0002677443,0.000615371,0.0003795546,0.001187834,0.0002921946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131474,"about_ca_system_score_gemma":0.0009271553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01529263,"about_ca_topic_score_gemma":0.01033427,"domain_scores_codex":[0.9995434,0.0002002669,0.00003395958,0.00009678956,0.00006287397,0.00006265489],"domain_scores_gemma":[0.9977413,0.00177275,0.0001752885,0.00004616252,0.0002324431,0.00003207975],"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":[0.0000383047,0.00005693656,0.001503066,0.00002089099,0.00002365847,0.00001623133,0.00001533074,0.9888486,0.0003271044,0.0002160651,0.0001215913,0.008812216],"study_design_scores_gemma":[0.000001973263,0.00001914188,0.000236871,0.000001918203,0.000002634596,0.000001859387,0.000002965992,0.9993881,0.0001303924,0.0001692496,0.00004261684,0.000002203343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6176512,0.0009943326,0.3746115,0.0005599346,0.00007456079,0.0002506379,0.0008061752,0.00106541,0.003986206],"genre_scores_gemma":[0.971245,0.00023927,0.02534224,0.00005191723,0.00002285235,0.0002628911,0.0005742344,0.00002097033,0.002240633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01529263,"threshold_uncertainty_score":0.03040719,"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."}}