{"id":"W2561611745","doi":"10.2166/wst.2016.600","title":"Chemically enhancing primary clarifiers: model-based development of a dosing controller and full-scale implementation","year":2016,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Canada Research Chairs","keywords":"Clarifier; Environmental science; Clogging; Settling; Particulates; Sewage treatment; Controller (irrigation); Process engineering; Environmental engineering; Waste management; Computer science; Engineering; Chemistry","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":[0.0004016041,0.0007900677,0.0006740338,0.0001738648,0.0002992506,0.0008623312,0.001279232,0.001027559,0.002110487],"category_scores_gemma":[0.0006179167,0.0003447738,0.0005438394,0.000129278,0.0003084792,0.0004379483,0.0004010813,0.0009239035,0.000345652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007013214,"about_ca_system_score_gemma":0.001304753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01078568,"about_ca_topic_score_gemma":0.007280061,"domain_scores_codex":[0.9998796,0.0000165257,0.000009639779,0.00003039385,0.0000484894,0.00001531646],"domain_scores_gemma":[0.9997563,0.0000778642,0.00003040825,0.00003448553,0.00008474687,0.00001625826],"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":[0.0001198504,0.0002257241,0.0007862368,0.0002429067,0.00002843909,0.00008026486,0.00004239826,0.927772,0.05224921,0.001081464,0.0002728366,0.01709859],"study_design_scores_gemma":[0.00003859367,0.0001856144,0.0003007563,0.000006021747,0.00001881151,0.00001168997,0.000008159584,0.9805712,0.01772806,0.0001501952,0.0009703359,0.00001062839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4700154,0.0006006816,0.507504,0.0004061799,0.000170966,0.001299263,0.0007196345,0.00344911,0.01583468],"genre_scores_gemma":[0.9182943,0.0002706762,0.07817518,0.00003040247,0.00001048842,0.0004700359,0.0002093745,0.00005891392,0.002480722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01078568,"threshold_uncertainty_score":0.02144575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006604342193767794,"score_gpt":0.2202311471291453,"score_spread":0.2136268049353775,"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."}}