{"id":"W2414317221","doi":"10.5194/isprs-archives-xli-b2-741-2016","title":"VISUALISATION OF DEPENDENCIES BETWEEN CITY STRUCTURE AND THERMAL BEHAVIOUR IN BRNO","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministerstvo Školství, Mládeže a Tělovýchovy","keywords":"Hyperspectral imaging; Remote sensing; Transect; Emissivity; Lidar; Overheating (electricity); Vegetation (pathology); Meteorology; Thermal; Geography; Data set; Urban heat island; Laser scanning; Environmental science; Computer science; Geology; Laser; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"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":[0.0001898343,0.000230711,0.0002474954,0.001801644,0.0002254234,0.000885843,0.0002615255,0.0002428086,0.002081577],"category_scores_gemma":[0.0005058424,0.0001986955,0.0003188225,0.001598164,0.0002113337,0.0002615706,0.0005705928,0.0002641346,0.0003572069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004744597,"about_ca_system_score_gemma":0.0003877195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04064969,"about_ca_topic_score_gemma":0.09305416,"domain_scores_codex":[0.9998684,0.00002026209,0.000005958765,0.00003969887,0.00003251922,0.00003307046],"domain_scores_gemma":[0.9997703,0.00006051919,0.00004450361,0.00002351912,0.00005327787,0.00004785289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001948639,0.0003820546,0.6412466,0.0009091289,0.0003694358,0.001596882,0.007564967,0.09828454,0.1063468,0.002302509,0.007310231,0.1317382],"study_design_scores_gemma":[0.00001772972,0.00003902178,0.9723446,0.00004935625,0.00002745832,0.00005898923,0.0008430257,0.02231242,0.0009218979,0.0001210224,0.003232473,0.00003199243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901484,0.0002021033,0.001631547,0.00007085292,0.00001686552,0.00002860053,0.002700405,0.0003659531,0.00483528],"genre_scores_gemma":[0.9956317,0.00006841955,0.00171252,0.000007688029,0.000004145918,0.00001476705,0.001753921,0.00005111658,0.0007558051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04064969,"threshold_uncertainty_score":0.08082616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01648608060411503,"score_gpt":0.2490390110761538,"score_spread":0.2325529304720388,"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."}}