{"id":"W2551310797","doi":"10.1002/met.1577","title":"Analysing heat exposure in two German cities by using meteorological data from both within and outside the urban area","year":2016,"lang":"en","type":"article","venue":"Meteorological Applications","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung","keywords":"Altitude (triangle); Urban heat island; Distribution (mathematics); Quarter (Canadian coin); Meteorology; Geography; Environmental science; Population; Daytime; Climatology; Physical geography; Atmospheric sciences; Demography; Mathematics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006029719,0.0001900421,0.0002488222,0.00003151379,0.0002548987,0.00005752561,0.0005922586,0.0001013886,0.0006686174],"category_scores_gemma":[0.00008951738,0.0000976203,0.00003636689,0.000274479,0.0006224484,0.0002733826,0.0005178507,0.0001795818,0.00005720886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009969275,"about_ca_system_score_gemma":0.000007864673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009913215,"about_ca_topic_score_gemma":0.000936063,"domain_scores_codex":[0.9981132,0.0002560743,0.0003800477,0.0007001746,0.000244283,0.0003061923],"domain_scores_gemma":[0.9984163,0.0005883018,0.00008571132,0.0007956828,0.00000674814,0.0001072638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004298603,0.0001534966,0.69091,0.000001978968,0.00004129655,0.000004843327,0.0004152116,0.0002596078,0.2977195,0.0007822087,0.002778558,0.006890297],"study_design_scores_gemma":[0.003115761,0.0003478298,0.8212591,0.00005003877,0.0005151029,0.00005176372,0.0006071841,0.04693491,0.008981859,0.08370826,0.0329776,0.00145061],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719922,0.0003502477,0.02489243,0.001461802,0.00001800621,0.0004556603,0.00022468,0.0000599918,0.0005449646],"genre_scores_gemma":[0.994888,0.00003352262,0.004034398,0.0006264774,0.00005546408,0.0001439914,0.0001144285,0.00001066619,0.00009306466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2887377,"threshold_uncertainty_score":0.7320892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04941801650773666,"score_gpt":0.2880028567644137,"score_spread":0.2385848402566771,"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."}}