{"id":"W3041817596","doi":"10.1111/1365-2745.13474","title":"Scale‐dependent changes in tree diversity over more than a century in eastern Canada: Landscape diversification and regional homogenization","year":2020,"lang":"en","type":"article","venue":"Journal of Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Montréal; Université de Sherbrooke; Université du Québec à Rimouski; Ministère des Ressources naturelles et des Forêts; Université du Québec en Abitibi-Témiscamingue","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Mitacs; Ministère des Forêts, de la Faune et des Parcs","keywords":"Biodiversity; Ecology; Geography; Homogenization (climate); Temperate rainforest; Diversification (marketing strategy); Temperate forest; Gamma diversity; Global change; Ecosystem; Context (archaeology); Ecosystem diversity; Beta diversity; Climate change; Biology","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":[0.000111838,0.00004893094,0.0001168332,0.00004502763,0.00006751142,0.000002571174,0.00008333284,0.00004818205,0.0001004512],"category_scores_gemma":[0.00002986403,0.00004214109,0.00001157897,0.00009688605,0.00006604103,0.00008717576,0.0001670827,0.0001045637,0.000002028224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001640256,"about_ca_system_score_gemma":0.00002513705,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005587965,"about_ca_topic_score_gemma":0.886706,"domain_scores_codex":[0.9995246,0.00005089191,0.000129431,0.00009578541,0.0001044867,0.00009482678],"domain_scores_gemma":[0.9997352,0.00004476051,0.0001373528,0.00002488959,0.0000112218,0.00004657665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005424895,0.00002841548,0.9959186,0.000003325863,0.000008180735,0.00003607024,0.002524743,0.0007149817,0.00005315428,0.000006815217,0.0002600307,0.0003913931],"study_design_scores_gemma":[0.000600229,0.00006599929,0.9955397,0.000003180163,0.000009123869,0.000009664986,0.001282228,0.002313665,0.000004769744,0.00004403351,0.00008531107,0.00004210189],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905256,0.00005570658,0.00001466832,0.009144708,0.00008220357,0.00005357024,0.000002616269,0.000001211626,0.0001196946],"genre_scores_gemma":[0.9990122,0.0002202199,0.00003726751,0.0006826127,0.0000204477,8.221252e-7,0.000002238821,0.000001719914,0.00002241531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8811181,"threshold_uncertainty_score":0.8447369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327891309981695,"score_gpt":0.2011306743977243,"score_spread":0.1878517612979073,"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."}}