{"id":"W2924347204","doi":"10.3390/su11061764","title":"Influent Forecasting for Wastewater Treatment Plants in North America","year":2019,"lang":"en","type":"article","venue":"Sustainability","topic":"Water resources management and optimization","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydromantis Environmental Software Solutions (Canada); McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Water Consortium","keywords":"Autoregressive integrated moving average; Wastewater; Watershed; Moving average; Environmental science; Sewage treatment; Time series; Effluent; Environmental engineering; Computer science; Statistics; Mathematics; Machine learning","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":[0.0003536927,0.0002843956,0.0002152226,0.0005899432,0.0003498294,0.0004435632,0.0002484337,0.0003298183,0.0006652628],"category_scores_gemma":[0.0009648871,0.0001720906,0.000237245,0.001230666,0.000090473,0.0004322394,0.0001564983,0.0003491694,0.0001241896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001379877,"about_ca_system_score_gemma":0.001214704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2161351,"about_ca_topic_score_gemma":0.2985862,"domain_scores_codex":[0.9998343,0.00002795513,0.00001084266,0.0000465162,0.00006145886,0.00001889255],"domain_scores_gemma":[0.9996418,0.0001344938,0.00005550943,0.00001727398,0.0001374587,0.00001341242],"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":[0.0001863291,0.0002049024,0.1793547,0.0001267404,0.0001046211,0.0002343589,0.0003390118,0.64044,0.007441649,0.00110024,0.007978503,0.162489],"study_design_scores_gemma":[0.000006873402,0.00003604116,0.06422338,0.000007729632,0.00001520422,0.00001725197,0.0002038017,0.9318975,0.001267377,0.0003920764,0.001916381,0.00001641242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746915,0.0002595367,0.01907537,0.0003459714,0.00002613325,0.00002942756,0.001480457,0.000453827,0.003637912],"genre_scores_gemma":[0.9892895,0.0001830885,0.007995991,0.00002065955,0.000005810152,0.00001783982,0.00129207,0.00001492533,0.001180311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2161351,"threshold_uncertainty_score":0.4297542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009569108584190628,"score_gpt":0.2027960152947701,"score_spread":0.1932269067105795,"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."}}