{"id":"W2145379412","doi":"10.1111/btp.12054","title":"Simulating Regional Vegetation‐climate Dynamics for Middle America: Tropical Versus Temperate Applications","year":2013,"lang":"en","type":"article","venue":"Biotropica","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biome; Vegetation (pathology); Temperate climate; Environmental science; Tropical vegetation; Shrub; Ecosystem; Tropics; Climatology; Physical geography; Geography; Atmospheric sciences; Ecology; Geology","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00001776206,0.0001181376,0.0001132085,0.00001700635,0.0002753738,0.00006240381,0.0001594214,0.0000638978,0.006692796],"category_scores_gemma":[0.00002838868,0.0001094215,0.00007631082,0.0001715135,0.0001666629,0.0001261933,0.00007667828,0.00006278876,0.002955338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004124316,"about_ca_system_score_gemma":0.000006471516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007782515,"about_ca_topic_score_gemma":0.0001321298,"domain_scores_codex":[0.999051,0.00001726121,0.0001924743,0.0002657779,0.0001640331,0.0003094507],"domain_scores_gemma":[0.9995062,0.00008300382,0.00007867973,0.0001971994,0.00002757162,0.0001073626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008396633,0.002240214,0.4154271,0.0004728625,0.0002911079,0.000004684714,0.001236442,0.003914058,0.05409146,0.301538,0.09767519,0.1222692],"study_design_scores_gemma":[0.002552971,0.0002800596,0.6655326,0.00001577884,0.00004807862,0.000003336431,0.001568568,0.05323455,0.0003146498,0.0006577171,0.2751895,0.0006022053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568854,0.00007980716,0.01708969,0.004752796,0.0002956837,0.001537832,0.0001767131,0.0002268984,0.01895519],"genre_scores_gemma":[0.9953022,0.00004182947,0.003043496,0.000412176,0.00009894774,0.0005357393,0.0002823972,0.00001639596,0.0002668161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3008803,"threshold_uncertainty_score":0.997821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04271355330132053,"score_gpt":0.2654156641541011,"score_spread":0.2227021108527805,"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."}}