{"id":"W2998725312","doi":"10.1002/9781119300762.wsts0168","title":"Optimization for Booster Chlorination","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Booster (rocketry); Chlorine; Odor; Haloacetic acids; Residual; Trihalomethane; Environmental science; Computer science; Water treatment; Waste management; Environmental engineering; Chemistry; Engineering; Algorithm","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":[0.0006756493,0.0009589704,0.0009626082,0.0004952316,0.0003267123,0.00105323,0.0005036987,0.0008462234,0.006571465],"category_scores_gemma":[0.001420564,0.0003362415,0.000832874,0.0004151169,0.0003336018,0.0005921078,0.0005035654,0.0009292468,0.0007019797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008564244,"about_ca_system_score_gemma":0.001150395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005334027,"about_ca_topic_score_gemma":0.003212507,"domain_scores_codex":[0.9997647,0.00005709823,0.000007301926,0.00005091991,0.00006396809,0.00005592917],"domain_scores_gemma":[0.9996533,0.0001923915,0.00003450264,0.00001939269,0.00008216409,0.00001824494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001204403,0.00007955272,0.0005030318,0.0002525098,0.00002719719,0.00007356988,0.00002614583,0.965057,0.004075809,0.005061404,0.001830309,0.02289317],"study_design_scores_gemma":[0.00001495506,0.00008790303,0.0003065236,0.00001792508,0.0000177756,0.0000134183,0.00001655,0.9939247,0.001793605,0.001837894,0.001962857,0.000005846243],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1594348,0.00412589,0.760527,0.001277219,0.000325058,0.0004097454,0.0007804098,0.001178308,0.07194171],"genre_scores_gemma":[0.8976845,0.001272754,0.08152303,0.00024923,0.0000414248,0.0002629875,0.0004218807,0.0001631615,0.01838105],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.006571465,"threshold_uncertainty_score":0.02198374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005239891968949983,"score_gpt":0.1847017508861985,"score_spread":0.1794618589172485,"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."}}