{"id":"W2970347340","doi":"10.1016/j.envint.2019.105080","title":"Quantifying the biophysical effects of forests on local air temperature using a novel three-layered land surface energy balance model","year":2019,"lang":"en","type":"article","venue":"Environment International","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Pearl River S and T Nova Program of Guangzhou; Guangdong Academy of Sciences; Water Resources Department of Guangdong Province; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Environmental science; Energy balance; Atmospheric sciences; Tree canopy; Canopy; Latitude; Forest ecology; Climate change; Ecosystem; Global warming; Air temperature; Climatology; Ecology; Geography","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":[0.00007889553,0.0001470241,0.0001234937,0.00001790645,0.00005620284,0.00001315072,0.0002614038,0.00006948118,0.0002427772],"category_scores_gemma":[0.000007556068,0.0001069422,0.00006561782,0.00004984543,0.0001235039,0.0001406754,0.0001396762,0.0001252938,0.0001180967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002449272,"about_ca_system_score_gemma":0.000007649167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001721841,"about_ca_topic_score_gemma":0.00005225141,"domain_scores_codex":[0.998821,0.00002395025,0.0001665894,0.0002900739,0.0005324393,0.0001659426],"domain_scores_gemma":[0.9995233,0.0001085669,0.00009040047,0.0002352461,0.000003353664,0.00003906507],"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":[0.00003104898,0.000115998,0.1367443,0.000005344531,0.00001534262,8.573223e-7,0.00005387263,0.5784528,0.2836148,0.0007981782,0.00006577691,0.0001016941],"study_design_scores_gemma":[0.0006239712,0.00007180625,0.1560499,0.00004819017,0.00001053683,0.000003729795,0.000007434633,0.7898975,0.05262415,0.0002509089,0.0002697612,0.000142087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9699063,0.00001461262,0.02904096,0.0001819661,0.000202153,0.000180828,0.00002784944,0.00001125802,0.0004340328],"genre_scores_gemma":[0.9982361,0.000008267435,0.00112588,0.0001931763,0.00004755377,0.000008026554,0.00002956905,0.00001674835,0.0003347273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2309906,"threshold_uncertainty_score":0.4360978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01285378374048259,"score_gpt":0.2179862044573631,"score_spread":0.2051324207168805,"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."}}