{"id":"W2763501459","doi":"10.1002/ecy.2045","title":"Explaining ecosystem multifunction with evolutionary models","year":2017,"lang":"en","type":"article","venue":"Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Niche; Species richness; Biology; Ecology; Ecosystem; Ecological niche; Phylogenetic diversity; Ecosystem diversity; Species diversity; Evolutionary ecology; Abundance (ecology); Trait; Neutral theory of molecular evolution; Coexistence theory; Functional ecology; Phylogenetic tree; Host (biology)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001271114,0.00007028106,0.00009944349,0.00001617155,0.0009937494,0.00001125785,0.0001427829,0.00006830136,0.0005235588],"category_scores_gemma":[0.00003567591,0.00005939314,0.00001706595,0.00001711848,0.0001735933,0.0003037929,0.0001419114,0.00007551956,0.0005619806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001094142,"about_ca_system_score_gemma":0.000007697954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003884477,"about_ca_topic_score_gemma":0.008187945,"domain_scores_codex":[0.9994485,0.00003437953,0.00009246187,0.0001970595,0.00005730145,0.0001702782],"domain_scores_gemma":[0.9995812,0.00005874802,0.0001148029,0.0002066781,0.000008790656,0.00002973746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003290152,0.00003908981,0.9479272,0.000003291509,0.00002504873,0.00001395215,0.0003176056,0.04713413,0.00003896929,0.002933811,0.001042047,0.0004919772],"study_design_scores_gemma":[0.0003207834,0.0001038,0.884074,0.00000275373,0.000008668039,0.00001896406,0.0001187712,0.1124987,0.000005593304,0.002207098,0.000560924,0.00007986249],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9362525,0.000008606376,0.002408148,0.0006016049,0.0003581115,0.0001204315,0.000002444488,0.00003576805,0.0602124],"genre_scores_gemma":[0.9965593,0.000007312497,0.001733628,0.0001043974,0.00002742542,0.00005630189,0.000003386611,0.000005761982,0.001502452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0653646,"threshold_uncertainty_score":0.7643217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01538795333445721,"score_gpt":0.2251481385940629,"score_spread":0.2097601852596057,"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."}}