{"id":"W2547393114","doi":"10.1680/jenes.15.00017","title":"A storm water basin model using settling velocity distribution","year":2016,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Settling; Environmental science; Sampling (signal processing); Storm; Hydrology (agriculture); Water quality; Structural basin; Particle (ecology); Calibration; Total suspended solids; Drainage basin; Suspended solids; Geology; Environmental engineering; Geomorphology; Oceanography; Wastewater; Geotechnical engineering; Mathematics; Statistics; Ecology; Computer science; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005685214,0.0001040053,0.0001040749,0.00005880461,0.0001786123,0.00003145619,0.0002005175,0.00002461653,0.00009972318],"category_scores_gemma":[0.00002148592,0.00006328965,0.00003903263,0.00008684145,0.0003500262,0.0008608063,0.0002313008,0.00007924694,0.00001707009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006989077,"about_ca_system_score_gemma":0.000007014178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000092431,"about_ca_topic_score_gemma":5.469225e-7,"domain_scores_codex":[0.9989046,0.000007804459,0.000210869,0.0001684011,0.0004182052,0.000290159],"domain_scores_gemma":[0.9996613,0.0000123106,0.00006808937,0.0001099979,0.000002952648,0.0001453616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006115282,0.00003366425,0.008415746,0.000002504913,0.000005627836,0.000005754278,0.0001889033,0.1205221,0.8693588,0.00002509878,0.0000650726,0.001370646],"study_design_scores_gemma":[0.001012549,0.0001685683,0.1952005,0.0001012557,0.00007103308,0.000255374,0.00009163415,0.7353424,0.06198806,0.0003055394,0.004894371,0.0005687249],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9092544,0.00001635077,0.09039854,0.0001414086,0.00009951983,0.00003870921,0.0000100326,0.000008880208,0.00003218131],"genre_scores_gemma":[0.9948303,0.00002025129,0.004994774,0.00002012887,0.00002520831,6.331263e-7,4.074809e-7,0.000007026971,0.0001012692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8073707,"threshold_uncertainty_score":0.2580877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00936718296683208,"score_gpt":0.1830242838389555,"score_spread":0.1736571008721234,"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."}}