{"id":"W2591889987","doi":"10.5194/bg-14-4733-2017","title":"An assessment of geographical distribution of different plant functional types over North America simulated using the CLASS–CTEM modelling framework","year":2017,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; University of Victoria; Université du Québec à Trois-Rivières; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Evergreen; Vegetation (pathology); Deciduous; Physical geography; Environmental science; Arid; Permafrost; Spatial distribution; Precipitation; Arctic; Climatology; Geography; Atmospheric sciences; Ecology; Geology; Remote sensing; Meteorology","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":[0.000133016,0.00008431439,0.0001129259,0.0000224833,0.0004161972,0.00005514522,0.0003395935,0.00005136814,0.00004635708],"category_scores_gemma":[0.00001095385,0.00005013591,0.00005216282,0.000147162,0.0006776092,0.0001823055,0.00009776128,0.00009188188,7.08289e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003262495,"about_ca_system_score_gemma":0.00001482026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001703408,"about_ca_topic_score_gemma":0.0001373168,"domain_scores_codex":[0.999037,0.00003766864,0.0001822692,0.0001925291,0.0004053758,0.0001452088],"domain_scores_gemma":[0.9993689,0.00005208075,0.0002367728,0.0002830077,0.00001228194,0.00004695921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005558199,0.00005751423,0.6485722,0.000001290145,0.000004728664,2.481803e-7,0.00002591196,0.3498628,0.0007839442,0.0005158432,0.000001399928,0.0001684995],"study_design_scores_gemma":[0.00002727445,0.0000307496,0.4525222,0.000006415542,0.00001076207,5.448723e-7,0.00001184531,0.5468941,0.00003394667,0.0003695842,0.00005266221,0.00003995989],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9321815,0.00000635088,0.06724931,0.00003956716,0.0001220845,0.00007188948,0.0002669256,0.000007142144,0.000055183],"genre_scores_gemma":[0.9990792,0.00002071792,0.0007724488,0.000009936814,0.00001459063,0.000001174923,0.00009540407,0.000002405294,0.000004065575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1970312,"threshold_uncertainty_score":0.3201094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02132135528846756,"score_gpt":0.2667262764822487,"score_spread":0.2454049211937812,"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."}}