{"id":"W4250843695","doi":"10.32920/ryerson.14663268","title":"Mapping and modelling urban solar energy potentials using geospacial data: A Case Study of Ryerson University Campus","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shading; Solar energy; Building energy simulation; Environmental science; HVAC; Workflow; Energy (signal processing); Software; Urban area; Orientation (vector space); Meteorology; Computer science; Civil engineering; Architectural engineering; Remote sensing; Geography; Energy performance; Engineering; Mathematics; Statistics; Geometry; Database; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004108284,0.0003596506,0.000251581,0.0006145815,0.0003987635,0.001136675,0.0005951052,0.0005600106,0.001275974],"category_scores_gemma":[0.0009124855,0.0002198209,0.0004490748,0.001486591,0.0002537,0.0004298114,0.0004544662,0.0002764171,0.0003345107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001582664,"about_ca_system_score_gemma":0.001017544,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2131642,"about_ca_topic_score_gemma":0.3231376,"domain_scores_codex":[0.9997751,0.00006743392,0.00001494593,0.0000504722,0.00005804332,0.00003402426],"domain_scores_gemma":[0.9995883,0.0001937312,0.00003339444,0.00006248871,0.00008565102,0.00003639356],"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":[0.0004501525,0.0003844377,0.1311021,0.0005054228,0.0001537021,0.003209287,0.003847458,0.7762118,0.01100786,0.003492253,0.004126229,0.06550927],"study_design_scores_gemma":[0.00004783027,0.000134708,0.1116406,0.0000670447,0.00007070886,0.0002238424,0.006687485,0.8584838,0.01298481,0.0007713119,0.008828638,0.00005929966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898225,0.00007759194,0.005271189,0.000122674,0.000004704642,0.00007449066,0.001971513,0.0002490039,0.002406328],"genre_scores_gemma":[0.9840643,0.000166343,0.01243521,0.000007290977,0.00000244472,0.00003754389,0.001971268,0.00004415241,0.001271385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7868358,"threshold_uncertainty_score":0.4238468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05478997449521076,"score_gpt":0.2347769850318012,"score_spread":0.1799870105365904,"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."}}