{"id":"W4234913032","doi":"10.22215/etd/2010-09164","title":"Thermal remote sensing of urban heat islands: Greater Toronto Area","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada","keywords":"Humanities; Art; Geography; Cartography","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":[0.00007204065,0.0002194967,0.0001035878,0.0004886353,0.0006846696,0.0005050193,0.0001940997,0.0001525708,0.00222034],"category_scores_gemma":[0.0001933752,0.0001021945,0.00009326236,0.001085091,0.0001721444,0.0001328087,0.0002030669,0.0001258191,0.0002043165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004056079,"about_ca_system_score_gemma":0.00215013,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9283117,"about_ca_topic_score_gemma":0.9811059,"domain_scores_codex":[0.9999298,0.000006343135,0.000003551389,0.00001660199,0.0000285037,0.00001519768],"domain_scores_gemma":[0.9998931,0.000009711653,0.00001227421,0.000006729157,0.00005709189,0.00002113043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003625314,0.00009992362,0.8363923,0.0005172475,0.0002215837,0.0009133567,0.005945128,0.01569379,0.03862621,0.001375634,0.02354922,0.07630304],"study_design_scores_gemma":[0.000004338947,0.00000785896,0.9916414,0.00001889756,0.00001291857,0.00002494687,0.0008587236,0.002387635,0.0006395859,0.00002068417,0.004377649,0.000005402832],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9810963,0.0006824539,0.0004699745,0.0002089828,0.00001803305,0.00004137241,0.00478555,0.00005378183,0.0126435],"genre_scores_gemma":[0.9914958,0.0003936744,0.0005527783,0.00002210752,0.00001070348,0.00001679278,0.001788859,0.000007613767,0.005711557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07168829,"threshold_uncertainty_score":0.1442209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008430592142124741,"score_gpt":0.2156401933484829,"score_spread":0.2072096012063581,"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."}}