{"id":"W4297147586","doi":"10.3390/cli10100138","title":"On the Intercontinental Transferability of Regional Climate Model Response to Severe Forestation","year":2022,"lang":"en","type":"article","venue":"Climate","topic":"Climate variability and models","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Ouranos","funders":"Biological and Environmental Research; Leibniz-Rechenzentrum; Leibniz-Gemeinschaft; Office of Science; Compute Canada; École de technologie supérieure; Bayerische Akademie der Wissenschaften; U.S. Department of Energy; National Science Foundation","keywords":"Climatology; Environmental science; Climate model; Snow; Albedo (alchemy); Shortwave radiation; Evergreen; Climate change; Afforestation; Geography; Ecology; Meteorology; Geology; Agroforestry; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002260117,0.0004846453,0.000325197,0.0003962491,0.0003918691,0.0006868322,0.0005753217,0.000591544,0.0009625693],"category_scores_gemma":[0.004678297,0.0002074008,0.000722144,0.0005531695,0.0004190743,0.0006573689,0.0009129634,0.000634237,0.0000989538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005032332,"about_ca_system_score_gemma":0.0004413095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02408856,"about_ca_topic_score_gemma":0.01833551,"domain_scores_codex":[0.9995769,0.0001720433,0.00002774597,0.0001170687,0.00005446132,0.00005186013],"domain_scores_gemma":[0.9987587,0.0005668684,0.0001315199,0.0002953861,0.0001657517,0.00008174212],"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":[0.0005868296,0.0001749676,0.117245,0.00006972433,0.0004996437,0.0001501163,0.0002787234,0.8559376,0.01287126,0.001412669,0.000794647,0.009978857],"study_design_scores_gemma":[0.0001193243,0.0003703267,0.2285258,0.00002540943,0.0001919061,0.00006271419,0.0001890454,0.7633638,0.004669908,0.0009289706,0.001475328,0.00007750714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957737,0.00006605568,0.001527494,0.0001223445,0.00002221587,0.00002096763,0.0005299843,0.0001049008,0.001832361],"genre_scores_gemma":[0.9986105,0.00002765437,0.0006843985,0.00002706123,0.000005489251,0.00001272793,0.0004555978,0.00002603813,0.0001506423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02408856,"threshold_uncertainty_score":0.04789668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02779730107575677,"score_gpt":0.2521970851372198,"score_spread":0.2243997840614631,"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."}}