{"id":"W4392793375","doi":"10.1016/j.rse.2024.114108","title":"Remote sensing of diverse urban environments: From the single city to multiple cities","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Science Foundation","keywords":"Remote sensing; Scope (computer science); Sustainability; Environmental planning; Land cover; Urban heat island; Field (mathematics); Urban planning; Land use; Environmental resource management; Geography; Environmental science; Computer science; Civil engineering; Meteorology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0005149792,0.0004682709,0.0004103442,0.001149826,0.0004396283,0.001119561,0.0004155724,0.0003454025,0.0007002835],"category_scores_gemma":[0.0004908562,0.0002990732,0.0004006416,0.002042022,0.0004690977,0.001283352,0.001488176,0.0003794665,0.0001071424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000418091,"about_ca_system_score_gemma":0.0004841665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02646753,"about_ca_topic_score_gemma":0.05921951,"domain_scores_codex":[0.9996599,0.00007520618,0.00001152103,0.0000910559,0.0000978701,0.00006450652],"domain_scores_gemma":[0.9998142,0.00005020586,0.00003282345,0.00003432956,0.00003845935,0.00002996197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005989174,0.0004722831,0.3945933,0.0004807868,0.0007248408,0.0008591975,0.001605745,0.08290586,0.06106992,0.004057995,0.008492489,0.4441387],"study_design_scores_gemma":[0.00005536031,0.0001238905,0.8811007,0.0001552635,0.0002893771,0.000606551,0.003578847,0.08906753,0.005233041,0.004689727,0.01500042,0.00009928687],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658878,0.00437701,0.01715546,0.001078021,0.00008983542,0.00005107058,0.001225739,0.00020211,0.009933069],"genre_scores_gemma":[0.9896148,0.001355454,0.007704037,0.0001106613,0.00007259971,0.00001696218,0.0005261408,0.00002364868,0.0005756514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02646753,"threshold_uncertainty_score":0.05262697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163821204522545,"score_gpt":0.2084570387945493,"score_spread":0.1868188267493239,"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."}}