{"id":"W2897128543","doi":"10.5194/gmd-12-785-2019","title":"The Air-temperature Response to Green/blue-infrastructure Evaluation Tool (TARGET v1.0): an efficient and user-friendly model of city cooling","year":2019,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Division of Chemical, Bioengineering, Environmental, and Transport Systems; Division of Social and Economic Sciences; Fonds Wetenschappelijk Onderzoek; Monash University; Australian Government; Cooperative Research Centre for Water Sensitive Cities; Arizona State University; Division of Earth Sciences; National Science Foundation","keywords":"Precinct; Scale (ratio); Computer science; Urban heat island; Environmental science; Climate change; Work (physics); CityGML; Climate model; Computation; Block (permutation group theory); Environmental economics; Meteorology; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00332038,0.0002522035,0.0002081569,0.0001035293,0.0005825231,0.0001094617,0.0004014274,0.0001253652,0.0001503205],"category_scores_gemma":[0.00009992102,0.0001922701,0.00004226369,0.0004032,0.0001497344,0.0002534246,0.0003535397,0.0001740313,0.00007253276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004771842,"about_ca_system_score_gemma":0.0003368217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004634245,"about_ca_topic_score_gemma":0.0001034987,"domain_scores_codex":[0.9967295,0.000176963,0.0004943706,0.0008120427,0.001312451,0.0004746917],"domain_scores_gemma":[0.9988279,0.00006099537,0.0001407693,0.0006535813,0.0001241105,0.0001926494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002656029,0.00004591254,0.00216032,0.00001202296,0.000007325532,3.148775e-7,0.004233315,0.908583,0.08046918,0.00006783179,0.001863131,0.002292018],"study_design_scores_gemma":[0.0004300061,0.00004795893,0.04741843,0.00002624045,0.00001353553,0.000002777156,0.0002259193,0.9378449,0.01091345,0.0001785514,0.002627254,0.0002710141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9649706,0.00002335588,0.03301077,0.0001593138,0.0003539697,0.001248354,0.00006798547,0.00003346602,0.000132147],"genre_scores_gemma":[0.9629172,0.000002143295,0.03225361,0.0001334575,0.00001003553,0.00007908085,0.00006405138,0.00001943456,0.004520977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06955573,"threshold_uncertainty_score":0.7840549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105311931542076,"score_gpt":0.2256455394824077,"score_spread":0.2151143463282001,"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."}}