{"id":"W2179349166","doi":"10.9734/bjecc/2015/11578","title":"Performance Evaluation of Low Impact Development Practices Using Linear Regression","year":2015,"lang":"en","type":"article","venue":"British Journal of Environment and Climate Change","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Low-impact development; Surface runoff; Watershed; Linear regression; Environmental science; Hydrology (agriculture); Regression analysis; Statistics; Sample (material); Stormwater; Engineering; Computer science; Civil engineering; Stormwater management; Mathematics; Geotechnical engineering; Machine learning","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":[0.01311299,0.001016298,0.0008860915,0.002358441,0.0005525547,0.002295713,0.001887292,0.0008719537,0.004573031],"category_scores_gemma":[0.05905845,0.0003082334,0.0008838308,0.003334859,0.0005839101,0.002024835,0.001452869,0.001040287,0.00140116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002711429,"about_ca_system_score_gemma":0.002500335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02025625,"about_ca_topic_score_gemma":0.01078735,"domain_scores_codex":[0.9879603,0.007142148,0.0006259548,0.001239192,0.002339753,0.0006926389],"domain_scores_gemma":[0.9182187,0.06028895,0.005232246,0.0041103,0.01084265,0.001307167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.005409722,0.003309176,0.1201614,0.0004838532,0.0004438184,0.0001243252,0.0005049065,0.5697799,0.00426009,0.002566918,0.001797609,0.2911582],"study_design_scores_gemma":[0.0002065989,0.005269647,0.03696293,0.00004557908,0.0001432113,0.00003676456,0.0006136688,0.9471978,0.007252505,0.001100681,0.001117471,0.0000530767],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9298911,0.0002689556,0.05954453,0.000292806,0.00003831226,0.0003126442,0.0006081435,0.001239992,0.00780354],"genre_scores_gemma":[0.9764237,0.0000864641,0.02124271,0.00001841206,0.000008964639,0.0001082926,0.0005137592,0.00008445214,0.001513341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02025625,"threshold_uncertainty_score":0.06934893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1635843761572716,"score_gpt":0.3267906344583872,"score_spread":0.1632062583011156,"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."}}