{"id":"W2909207322","doi":"10.1080/09640568.2018.1496072","title":"Understanding barriers to green infrastructure policy and stormwater management in the City of Toronto: a shift from grey to green or policy layering and conversion?","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Planning and Management","topic":"Sustainable Building Design and Assessment","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Green infrastructure; Stormwater management; Grey literature; Stormwater; Policy analysis; Business; Public policy; Urban policy; Environmental planning; Urban planning; Public administration; Environmental resource management; Economics; Civil engineering; Economic growth; Political science; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002427532,0.0001591302,0.000211326,0.0002032392,0.00005734816,0.00005202583,0.0001366677,0.00003541811,0.00003967043],"category_scores_gemma":[0.00000390477,0.000117193,0.00002373042,0.00007556949,0.00002451431,0.0001853292,0.0001964528,0.0001031066,5.057368e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005095861,"about_ca_system_score_gemma":0.000008037336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006074473,"about_ca_topic_score_gemma":0.00002748596,"domain_scores_codex":[0.999129,0.00002701222,0.0002417307,0.0001482272,0.0002355216,0.000218536],"domain_scores_gemma":[0.9996133,0.00003769228,0.00006231364,0.0001153202,0.000002074227,0.0001692693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0022391,0.0001869123,0.7524908,0.003172157,0.002663966,0.001556312,0.0984026,0.07582534,0.007764057,0.005842652,0.003220087,0.04663597],"study_design_scores_gemma":[0.002302165,0.0005129801,0.9265783,0.0004590697,0.0001161414,0.00003194261,0.05982734,0.0008104412,0.0001164876,0.001024366,0.007853886,0.0003669151],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965662,0.0002574385,0.001522208,0.0004082084,0.00006292234,0.0003666975,0.00001034932,0.000007464947,0.0007985002],"genre_scores_gemma":[0.9979979,0.0003367668,0.001069535,0.0003853301,0.00005612641,0.000004128276,0.000001187359,0.00001401558,0.0001350428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1740874,"threshold_uncertainty_score":0.477899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132436310071347,"score_gpt":0.2387513342814056,"score_spread":0.2255077032742709,"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."}}