{"id":"W2056280096","doi":"10.1111/j.1466-8238.2010.00532.x","title":"What does species richness tell us about functional trait diversity? Predictions and evidence for responses of species and functional trait diversity to land‐use change","year":2010,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":461,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service","funders":"","keywords":"Trait; Ecology; Species richness; Context (archaeology); Ecosystem; Environmental change; Global change; Biodiversity; Functional diversity; Environmental resource management; Diversity (politics); Disturbance (geology); Biology; Species diversity; Climate change; Environmental science; Computer science","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":[0.003417336,0.0002995467,0.0004426716,0.001088651,0.0003363189,0.001268436,0.0006307155,0.001159258,0.002670892],"category_scores_gemma":[0.01739698,0.0003514317,0.0007051781,0.0008626169,0.002322105,0.002168329,0.0007298558,0.000856136,0.0004377459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004443743,"about_ca_system_score_gemma":0.0001079406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002516311,"about_ca_topic_score_gemma":0.002133828,"domain_scores_codex":[0.9991595,0.0004321519,0.00003342356,0.0002086456,0.00007696181,0.00008935733],"domain_scores_gemma":[0.9682712,0.02532699,0.003205611,0.001641018,0.0008457657,0.000709419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004689876,0.0001074244,0.9579386,0.0001395163,0.0005188829,0.0001945989,0.000652671,0.01196256,0.003178167,0.004012793,0.0005451268,0.02028063],"study_design_scores_gemma":[0.00001568484,0.0001285344,0.9499944,0.00003772622,0.0001064812,0.0002435216,0.0008164379,0.02513496,0.0008580883,0.02196494,0.0006670502,0.00003226058],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917734,0.0007710333,0.002960432,0.001593045,0.00001752996,0.000003292221,0.0002712719,0.00002136376,0.002588791],"genre_scores_gemma":[0.9994516,0.0001295161,0.0001714344,0.00009817086,0.00001305351,0.000001573627,0.00005937438,0.000003217993,0.00007213485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003417336,"threshold_uncertainty_score":0.01807284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.035732106865952,"score_gpt":0.236567988282329,"score_spread":0.200835881416377,"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."}}