{"id":"W2103379079","doi":"10.3390/rs3071380","title":"Geospatial Technologies to Improve Urban Energy Efficiency","year":2011,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Geospatial analysis; Urban heat island; Computer science; Environmental science; Efficient energy use; Remote sensing; Architectural engineering; Database; Meteorology; Geography; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008397093,0.0001069669,0.00009175653,0.00004582003,0.00009512437,0.00001335828,0.0001182264,0.00007966849,0.0000581808],"category_scores_gemma":[0.00006475315,0.00009787823,0.00003101393,0.0002197427,0.00008182088,0.00007774784,0.0001585295,0.00007055669,0.0002697893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001023725,"about_ca_system_score_gemma":0.000006295211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002119095,"about_ca_topic_score_gemma":0.0002798897,"domain_scores_codex":[0.9991229,0.00001872542,0.0001330605,0.0002855193,0.0001630634,0.0002767514],"domain_scores_gemma":[0.99961,0.0000143136,0.00003723035,0.0002781273,0.000007279573,0.00005306067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000112888,0.00001350565,0.0004628121,0.0000021043,0.000003400343,0.00001845615,0.001228395,0.0000464837,0.172038,0.000107219,0.0008748797,0.8251935],"study_design_scores_gemma":[0.0003850442,0.0003700791,0.005240301,0.0000541313,0.00002782322,0.00005321559,0.0004356427,0.1207096,0.8562797,0.007232719,0.008530535,0.0006811417],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8465343,0.00002050074,0.1107138,0.0001619044,0.0003030703,0.0001533706,0.000001176062,0.0003801344,0.04173181],"genre_scores_gemma":[0.9724267,0.00000335867,0.02673837,0.0001306416,0.00003569514,2.964807e-8,0.000001193909,0.00001314493,0.000650842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8245123,"threshold_uncertainty_score":0.3991358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01054801625993265,"score_gpt":0.1910854219257153,"score_spread":0.1805374056657826,"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."}}