{"id":"W4252808552","doi":"10.5194/isprsarchives-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":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministerstvo Školství, Mládeže a Tělovýchovy","keywords":"Hyperspectral imaging; Transect; Emissivity; Remote sensing; Lidar; Urban heat island; Meteorology; Vegetation (pathology); Thermal; Overheating (electricity); Laser scanning; Geography; Data set; 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":[0.0001695606,0.0002136756,0.0002529761,0.001682121,0.0002575208,0.0009317899,0.0002667941,0.0002702853,0.00187651],"category_scores_gemma":[0.0005302459,0.0001933001,0.0003033162,0.001445107,0.000290797,0.0002818658,0.0006936894,0.0002737626,0.0002885977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005215351,"about_ca_system_score_gemma":0.0004221611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04502134,"about_ca_topic_score_gemma":0.09953941,"domain_scores_codex":[0.9998764,0.00001842796,0.000005951424,0.00003552861,0.00002971013,0.00003399274],"domain_scores_gemma":[0.9997557,0.00006677424,0.00005102955,0.00002294476,0.00004550963,0.00005805851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0019499,0.000392712,0.6354239,0.0008897314,0.0003643128,0.002356268,0.01186525,0.1134607,0.1055427,0.003926823,0.007084464,0.1167434],"study_design_scores_gemma":[0.00001850356,0.00003664368,0.9760884,0.00006202052,0.00002681285,0.00007641418,0.001095504,0.01848087,0.0007217297,0.0001617193,0.003196402,0.00003499437],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909895,0.0002408365,0.001131289,0.00009553328,0.00001463712,0.00002751925,0.001814136,0.0002652961,0.005421245],"genre_scores_gemma":[0.9962353,0.0001084245,0.001494158,0.00001128621,0.000006001933,0.00001531353,0.001222744,0.00004586089,0.0008608403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04502134,"threshold_uncertainty_score":0.08951861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474002672403291,"score_gpt":0.2475435893784614,"score_spread":0.2328035626544285,"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."}}