{"id":"W4226050648","doi":"10.1039/d1ew00646k","title":"Including snowmelt in influent generation for cold climate WRRFs: comparison of data-driven and phenomenological approaches","year":2022,"lang":"en","type":"article","venue":"Environmental Science Water Research & Technology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; CentrEau - Quebec Water Management Research Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Snowmelt; Phenomenological model; Environmental science; Climatology; Climate change; Atmospheric sciences; Phenomenology (philosophy); Meteorology; Hydrology (agriculture); Snow; Engineering; Geology; Mathematics; Physics; Geotechnical 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":[0.0006245902,0.0006393492,0.0007229586,0.0003821169,0.0005289902,0.0007781116,0.001173789,0.001167443,0.001021595],"category_scores_gemma":[0.001557018,0.000475154,0.0009798496,0.000341861,0.0005405518,0.000982423,0.0005376706,0.0007355693,0.0001359357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255621,"about_ca_system_score_gemma":0.001580179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03811247,"about_ca_topic_score_gemma":0.02131343,"domain_scores_codex":[0.9998579,0.00004832285,0.00001178394,0.00003224859,0.00002488966,0.00002478794],"domain_scores_gemma":[0.9992462,0.0004126246,0.00006225287,0.00006762726,0.0001495538,0.00006179074],"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":[0.0000203338,0.00002531147,0.000766205,0.00001517182,0.000006797649,0.00002577702,0.00001714296,0.9969317,0.0004736034,0.0002725619,0.00003135321,0.001414049],"study_design_scores_gemma":[0.000009740033,0.00001386466,0.0002145275,0.000002525868,0.000003288062,0.000002897264,0.000008026394,0.9991246,0.0003472111,0.0001994351,0.00006853096,0.000005427435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7494798,0.0002177831,0.2415467,0.0005267164,0.0001036884,0.0002796102,0.001000701,0.0008060986,0.006038828],"genre_scores_gemma":[0.991515,0.00006820694,0.007504173,0.00002710359,0.00001097661,0.00006882637,0.0001994513,0.00003065536,0.0005755106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03811247,"threshold_uncertainty_score":0.07578123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2369097930935153,"score_gpt":0.372331226195087,"score_spread":0.1354214331015717,"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."}}