{"id":"W4392808101","doi":"10.1016/j.jenvman.2024.120510","title":"Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Effluent; Wastewater; Sewage treatment; Chemical oxygen demand; Genetic programming; Water quality; Environmental science; Process (computing); Biochemical engineering; Environmental engineering; Process engineering; Computer science; Engineering; Machine learning; Ecology; Biology","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":[0.001104899,0.0009242486,0.0006941484,0.0006858011,0.0002656008,0.00103297,0.001119659,0.001123438,0.0007306297],"category_scores_gemma":[0.003596862,0.0003383161,0.000661357,0.0007572569,0.0006038967,0.0005479413,0.000892924,0.0013258,0.0001591233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009372275,"about_ca_system_score_gemma":0.001461033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009766595,"about_ca_topic_score_gemma":0.006468802,"domain_scores_codex":[0.999523,0.0001853625,0.0000260395,0.00009448151,0.000125068,0.00004592132],"domain_scores_gemma":[0.9989195,0.0006822255,0.000135689,0.00002356522,0.0002036006,0.00003545137],"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":[0.00001252313,0.0000263416,0.0005288686,0.00002458335,0.00001361367,0.00003543626,0.00002966324,0.980247,0.0004980417,0.003086351,0.0002666972,0.01523074],"study_design_scores_gemma":[0.000001609641,0.000005292498,0.00003219936,0.000003737039,0.000001865145,0.000002333827,0.000003638754,0.9981738,0.0001164785,0.001518642,0.000139169,0.000001198779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02942938,0.0004016258,0.9663145,0.0007236076,0.00003379096,0.0000761972,0.0001162301,0.0003533222,0.002551387],"genre_scores_gemma":[0.5594713,0.0007684536,0.4356676,0.0004772734,0.00008654375,0.0004806769,0.0003906024,0.0001162862,0.002541236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009766595,"threshold_uncertainty_score":0.01941949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0339840695778516,"score_gpt":0.2937172924410996,"score_spread":0.259733222863248,"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."}}