{"id":"W1487527678","doi":"10.35196/rfm.2012.4.333","title":"SPLINE MODELS OF CONTEMPORARY, 2030, 2060 AND 2090 CLIMATES FOR MICHOACÁN STATE, MÉXICO. IMPACTS ON THE VEGETATION","year":2012,"lang":"en","type":"article","venue":"Revista Fitotecnia Mexicana","topic":"Plant and soil sciences","field":"Agricultural and Biological Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Intégré de Santé et de Services Sociaux des Laurentides; Ministère des Ressources naturelles et des Forêts (Québec)","funders":"Coordinación de la Investigación Científica; Natural Resources Canada; Universidad Michoacana de San Nicolás de Hidalgo; Canadian Forest Service; Consejo Nacional de Ciencia y Tecnología","keywords":"Climatology; Environmental science; Precipitation; Climate model; Aridity index; Climate change; Arid; Mean radiant temperature; Representative Concentration Pathways; Geography; Atmospheric sciences; Meteorology; Ecology; Geology","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":[0.0004470397,0.0002800683,0.0001750772,0.0005145749,0.0003931702,0.0004181098,0.0006134222,0.0003287018,0.002318285],"category_scores_gemma":[0.0007902736,0.0002011138,0.0005900388,0.0007360395,0.0001998036,0.0002646925,0.0003169151,0.000337697,0.0002193931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001489349,"about_ca_system_score_gemma":0.001120039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2028191,"about_ca_topic_score_gemma":0.25979,"domain_scores_codex":[0.9999204,0.00002651613,0.000003702064,0.00002231209,0.000009423047,0.00001770092],"domain_scores_gemma":[0.9997824,0.00007765385,0.00004231677,0.00001700465,0.00005520132,0.00002548809],"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":[0.0001798754,0.0000776227,0.1093549,0.00003680735,0.00009981487,0.00008378516,0.0001350189,0.8775948,0.0004311087,0.003447361,0.002506835,0.006052043],"study_design_scores_gemma":[0.00007888555,0.00006829118,0.0997954,0.00001980454,0.00006220835,0.00003168768,0.000208173,0.8928648,0.0001604443,0.001749704,0.004934894,0.00002559887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824888,0.0001130712,0.00592095,0.0002750553,0.00002348798,0.00002382305,0.007539908,0.0001788744,0.003436015],"genre_scores_gemma":[0.9886295,0.0001014864,0.002718331,0.00002199355,0.00001062537,0.00007265958,0.006090362,0.00001616655,0.002338906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2028191,"threshold_uncertainty_score":0.4032772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05987719404104243,"score_gpt":0.2531885449892188,"score_spread":0.1933113509481764,"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."}}