{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0001805842,0.0002311072,0.0002243671,0.00001572727,0.001427985,0.00003324232,0.0004481442,0.0001571224,0.00007150853],"category_scores_gemma":[0.00001097961,0.0002034642,0.00006186816,0.00004157374,0.0003862632,0.0002100299,0.0006508122,0.0001312344,0.0002641028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000494302,"about_ca_system_score_gemma":0.00000131228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001435796,"about_ca_topic_score_gemma":0.006089753,"domain_scores_codex":[0.9988318,0.00024283,0.0001386945,0.0002929402,0.0001161976,0.0003775393],"domain_scores_gemma":[0.9993277,0.00004210414,0.0001462305,0.0004094572,0.000002388752,0.00007218558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001159933,0.0002025885,0.9845581,0.00002576273,0.00007682054,0.00003301042,0.000393098,0.000002406626,0.00005152418,0.005364499,0.0002569646,0.008919218],"study_design_scores_gemma":[0.0005375221,0.0004471259,0.9950558,0.00001718736,0.00004954926,0.00001728044,0.0001419819,0.0001196729,0.00002622339,0.002388458,0.0009973134,0.0002019001],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887998,0.000183317,0.0000505231,0.0005927183,0.0009012995,0.0005486986,0.0005664094,0.00005413865,0.008303069],"genre_scores_gemma":[0.9982495,0.0002441449,0.0001065916,0.0007968464,0.0001349563,0.0001985769,0.0002411614,0.000005116349,0.0000231417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01049767,"threshold_uncertainty_score":0.999872,"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."}}