{"id":"W2806583880","doi":"","title":"Snow, vegetation, and asymmetric canyons in the Canadian version of the Town Energy Balance (TEB) urban-canopy scheme","year":2010,"lang":"en","type":"article","venue":"29th Conf. on Agricultural and Forest Meteorology/19th Symp. on Boundary Layers and Turbulence/Ninth Symp. on the Urban Environment (1-6 August 2010)","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Snow; Canyon; Vegetation (pathology); Energy balance; Environmental science; Canopy; Meteorology; Geography; Cartography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004165854,0.0001621503,0.0002111943,0.0006296752,0.001407113,0.001050444,0.0008951809,0.0002360962,0.005467714],"category_scores_gemma":[0.001233107,0.00008559998,0.000181297,0.001573072,0.0003805351,0.0004847555,0.00044819,0.0003166536,0.0002637875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009946523,"about_ca_system_score_gemma":0.01179801,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9732758,"about_ca_topic_score_gemma":0.9903093,"domain_scores_codex":[0.9997482,0.00002702559,0.000008907759,0.00002722952,0.00008608271,0.0001025484],"domain_scores_gemma":[0.9996085,0.00002494018,0.00001558623,0.00004952848,0.0002365695,0.00006491901],"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":[0.0006267814,0.0001460248,0.1833107,0.00008957907,0.00009058437,0.000213443,0.000575666,0.3136367,0.001608309,0.2447104,0.05718109,0.1978107],"study_design_scores_gemma":[0.00008256493,0.0000506517,0.2982138,0.0000989425,0.00009313857,0.00009843361,0.001857822,0.5752814,0.001301042,0.02680723,0.09599958,0.0001154382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8191217,0.0006774439,0.02461569,0.001428368,0.0001366557,0.0002442739,0.01887344,0.0002704709,0.134632],"genre_scores_gemma":[0.9796862,0.0001175067,0.004849946,0.00004094211,0.000007064349,0.00002004583,0.003456505,0.00003594542,0.01178597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02672416,"threshold_uncertainty_score":0.07216746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005311635824091925,"score_gpt":0.1682067317437159,"score_spread":0.162895095919624,"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."}}