{"id":"W2779029320","doi":"10.1111/gcb.14030","title":"Global environmental change effects on plant community composition trajectories depend upon management legacies","year":2017,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"European Research Council; Vlaamse regering; Universiteit Gent; Grantová Agentura České Republiky; Fonds Wetenschappelijk Onderzoek; Agentúra na Podporu Výskumu a Vývoja","keywords":"Species richness; Understory; Environmental change; Forest management; Ecology; Plant community; Temperate forest; Global change; Temperate rainforest; Geography; Coppicing; Trait; Indicator value; Climate change; Environmental resource management; Temperate climate; Ecosystem; Environmental science; Woody plant; Canopy; 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":[0.000776845,0.0002145546,0.000231616,0.0005792066,0.0002177947,0.0006017579,0.0001672453,0.0002432581,0.001100974],"category_scores_gemma":[0.001480383,0.0001293423,0.000456622,0.0005744367,0.0003985518,0.0004063311,0.000567881,0.0002340851,0.0001284495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002754574,"about_ca_system_score_gemma":0.0001380824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004317983,"about_ca_topic_score_gemma":0.01011935,"domain_scores_codex":[0.9996611,0.0001057623,0.00002539821,0.0001324608,0.00002602854,0.00004928645],"domain_scores_gemma":[0.9989478,0.0003557396,0.000348659,0.000144317,0.00006982229,0.0001336882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000974438,0.00001684092,0.9885533,0.00002162412,0.000177891,0.00003494484,0.0001591334,0.0004989515,0.005689753,0.00005605704,0.00006573976,0.004628409],"study_design_scores_gemma":[7.258192e-7,0.00001095044,0.9995381,0.000001356703,0.00000864804,0.000008832992,0.00005048147,0.0002192645,0.0000636805,0.00001603934,0.00008059618,0.000001400501],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991058,0.0001275569,0.0001793091,0.00002000205,0.000001793084,0.00000217433,0.0002788428,0.000007115708,0.0002773703],"genre_scores_gemma":[0.999342,0.00005291347,0.0001345903,0.0000118003,0.000001632426,0.000003426363,0.0003909905,0.00000339962,0.00005906698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004317983,"threshold_uncertainty_score":0.008585691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618190500184368,"score_gpt":0.2777837578201261,"score_spread":0.2416018528182824,"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."}}