{"id":"W7133033325","doi":"","title":"Utilizing Mobile Monitoring to Predict the Spatial Distribution of Urban Air Temperature and Associations of Marginalization at a Microscale in Mississauga, Ontario","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto Mississauga; University of Toronto","keywords":"Air temperature; Urban heat island; Data collection; Microscale chemistry; Sampling (signal processing); Surface air temperature; Regression analysis; Spatial distribution; Autoregressive model","routes":{"ca_aff":false,"ca_fund":true,"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":[0.0001693022,0.0002768655,0.0001924089,0.0002520886,0.0008335424,0.000671648,0.000560072,0.0002271395,0.0007518127],"category_scores_gemma":[0.0009385066,0.0001646812,0.0002631533,0.0007000637,0.0002829013,0.0002346006,0.0003233165,0.0002819722,0.0001310388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009151186,"about_ca_system_score_gemma":0.008660841,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9876123,"about_ca_topic_score_gemma":0.9948812,"domain_scores_codex":[0.9998521,0.00001618707,0.000005206613,0.00004294113,0.00003689158,0.00004658007],"domain_scores_gemma":[0.9996781,0.00005702831,0.00005016381,0.00001731368,0.0001573635,0.00004006538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002524133,0.00005351894,0.9245993,0.0001083674,0.0001383741,0.000200514,0.003956793,0.02450266,0.006709317,0.000688221,0.002530071,0.03626031],"study_design_scores_gemma":[0.000009895784,0.00002799895,0.9621306,0.00003044383,0.00002719124,0.00001677602,0.002572979,0.031647,0.0005603898,0.00007249373,0.002887018,0.00001734609],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948334,0.0001233328,0.000970303,0.0001601653,0.000003303044,0.00002347397,0.001113588,0.00002800161,0.002744357],"genre_scores_gemma":[0.9974723,0.00009972001,0.0008882956,0.0000102338,0.000001855472,0.00001418224,0.0004813826,0.000004977211,0.001026906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01238769,"threshold_uncertainty_score":0.06639683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01397469495744564,"score_gpt":0.2774785655811955,"score_spread":0.2635038706237499,"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."}}