{"id":"W2130174968","doi":"10.1890/07-0693.1","title":"PREDICTING TREE DIVERSITY ACROSS THE UNITED STATES AS A FUNCTION OF MODELED GROSS PRIMARY PRODUCTION","year":2008,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Primary production; Species richness; Vegetation (pathology); Productivity; Forest inventory; Biodiversity; Environmental science; Ecology; Physical geography; Hectare; Geography; Spatial ecology; Scale (ratio); Ecosystem; Forest management; Agroforestry; Cartography; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006015134,0.0003219205,0.000221466,0.0005434989,0.0002471085,0.000496175,0.0003391298,0.0002107763,0.0004818196],"category_scores_gemma":[0.001511376,0.0002583298,0.0003882156,0.0006465486,0.0001727482,0.0003137405,0.0003346782,0.0002290771,0.0001062185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007944552,"about_ca_system_score_gemma":0.000376822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1029207,"about_ca_topic_score_gemma":0.143011,"domain_scores_codex":[0.9998392,0.00005021358,0.000009832209,0.00006338942,0.0000172997,0.00002014743],"domain_scores_gemma":[0.9992396,0.0003632373,0.0001257397,0.00006884163,0.0001288521,0.00007373228],"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":[0.00007029636,0.00004530873,0.9101349,0.000009250277,0.00009630153,0.00005788838,0.00006398225,0.08440942,0.0003796016,0.0001895958,0.0004121111,0.004131312],"study_design_scores_gemma":[0.00002420793,0.00007821887,0.6827579,0.00001652939,0.00007808843,0.00006298887,0.0002186392,0.3152184,0.0005148082,0.0004362328,0.0005780071,0.00001594575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981792,0.00004690039,0.000675583,0.00002083492,0.000001374665,0.000005138011,0.0006345492,0.00002509461,0.000411414],"genre_scores_gemma":[0.9978783,0.00005585633,0.000804442,0.000009816071,0.000001389785,0.00000800074,0.001152309,0.000003551472,0.00008635594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1029207,"threshold_uncertainty_score":0.2046433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641829429661363,"score_gpt":0.214309371908544,"score_spread":0.1978910776119304,"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."}}