{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072913,0.0001286883,0.0002117759,0.00001329185,0.0001774992,0.00005891986,0.0001917451,0.00005293202,0.0000157596],"category_scores_gemma":[0.00009635984,0.00004049788,0.0000664616,0.0001762599,0.000104509,0.000250553,0.00003122827,0.00006922847,0.000004583914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001123658,"about_ca_system_score_gemma":0.00001317411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000379459,"about_ca_topic_score_gemma":0.0001933161,"domain_scores_codex":[0.9990365,0.0000633177,0.0002483897,0.0001728552,0.0001896832,0.0002892891],"domain_scores_gemma":[0.9990141,0.0005702524,0.0001690207,0.00006225052,0.0000553236,0.0001290804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007215958,0.0006836687,0.1118666,0.0005262895,0.000162914,0.000003523477,0.002009962,0.000166202,0.699197,0.07354767,0.02470065,0.08641391],"study_design_scores_gemma":[0.001362405,0.002742866,0.8179052,0.0009797884,0.0001284864,0.00002733271,0.001952438,0.01649638,0.06140744,0.02020806,0.07516383,0.001625762],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943365,0.002851835,0.00001213976,0.001242952,0.0000643463,0.0003729556,0.0001732494,0.00001881746,0.0009272441],"genre_scores_gemma":[0.9990792,0.0003558822,0.00005444942,0.0001988682,0.0001331514,0.00001485362,0.00004345021,0.000001051579,0.0001190753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7060386,"threshold_uncertainty_score":0.1651456,"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."}}