{"id":"W2336253119","doi":"10.2166/wst.2001.0440","title":"Ranking stormwater control strategies under uncertainty: the River Cam case study","year":2001,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Ranking (information retrieval); Water quality; Environmental science; Stormwater; Hydrology (agriculture); Environmental engineering; Wastewater; Dominance (genetics); Rank (graph theory); Monte Carlo method; Sewage treatment; Statistics; Mathematics; Computer science; Engineering; Surface runoff; Geotechnical engineering","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":[0.004052538,0.0007518374,0.001109753,0.001152935,0.0005151522,0.00100419,0.0006580157,0.001060325,0.001011942],"category_scores_gemma":[0.01094726,0.0003245602,0.0006425918,0.0009115908,0.0005937458,0.0008887055,0.0004612643,0.0005485953,0.00005017883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001800258,"about_ca_system_score_gemma":0.001119914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02197818,"about_ca_topic_score_gemma":0.0225694,"domain_scores_codex":[0.9987612,0.0007247777,0.00004906376,0.00008918323,0.0001960685,0.0001796712],"domain_scores_gemma":[0.9915935,0.007015388,0.0004895944,0.0002337267,0.0005053931,0.0001624231],"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":[0.0001504437,0.0000584651,0.004008495,0.00003042153,0.00006278847,0.0001146172,0.00003364702,0.9893081,0.0002924702,0.001387236,0.0001236084,0.004429918],"study_design_scores_gemma":[0.00007213987,0.0004121263,0.002110756,0.000008347411,0.00004074868,0.00003329184,0.000105232,0.9944296,0.001010886,0.001527173,0.0002300125,0.00001970748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889892,0.0001002598,0.009315444,0.0001073623,0.000004615569,0.00007292897,0.0001172803,0.00002971053,0.001263226],"genre_scores_gemma":[0.9952743,0.00005241671,0.00424342,0.00001216157,0.000002245905,0.0000277553,0.00007579102,0.000004929321,0.0003069721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02197818,"threshold_uncertainty_score":0.04370046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281463376172683,"score_gpt":0.2394141119362377,"score_spread":0.2265994781745108,"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."}}