{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001948662,0.000122263,0.0001481773,0.00003377651,0.0003528091,0.00001352014,0.0003105625,0.00002734618,0.002146679],"category_scores_gemma":[0.00008946748,0.00009701951,0.0001059101,0.0001835755,0.0001337208,0.0001066648,0.0003722245,0.0001673219,0.00007397294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002353673,"about_ca_system_score_gemma":0.00001298491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004951169,"about_ca_topic_score_gemma":0.0000655815,"domain_scores_codex":[0.9983619,0.0003120825,0.0003095929,0.0003335419,0.0003785264,0.0003043576],"domain_scores_gemma":[0.9990543,0.0004076431,0.0000614778,0.0003997754,0.000009176612,0.00006766721],"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":[0.01096461,0.0006126508,0.006819533,0.0000346211,0.00001085759,0.000003239177,0.006939534,0.8896814,0.06388785,0.01844658,0.001944951,0.0006541856],"study_design_scores_gemma":[0.002010374,0.001711096,0.08083154,0.00006214578,0.0000574408,0.00003668333,0.003064169,0.8592095,0.002255789,0.04690076,0.003099455,0.0007609926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923751,0.000001615501,0.0005408325,0.004044299,0.00006195068,0.000469478,0.0004140283,0.00003121676,0.002061486],"genre_scores_gemma":[0.9982315,0.00000806294,0.000304704,0.001215181,0.000004171297,0.00013332,0.00002443656,0.00001341178,0.00006520895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07401201,"threshold_uncertainty_score":0.9987655,"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."}}