{"id":"W3088738889","doi":"10.1111/geb.13181","title":"Functional and phylogenetic diversity promote litter decomposition across terrestrial ecosystems","year":2020,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Decomposer; Litter; Plant litter; Ecosystem; Species richness; Ecology; Abundance (ecology); Nutrient cycle; Biology; Phylogenetic diversity; Biomass (ecology); Terrestrial ecosystem; Monoculture; Phylogenetics","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":[0.00009408592,0.0001012469,0.0001237866,0.00001074083,0.0006295841,0.00001554706,0.00006087998,0.0001195657,0.0001284879],"category_scores_gemma":[0.00001024202,0.00009355834,0.00004236298,0.0001480473,0.0004091974,0.00007050596,0.0004574276,0.00006618775,0.0000512909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001769612,"about_ca_system_score_gemma":0.000002575699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008085353,"about_ca_topic_score_gemma":0.001837816,"domain_scores_codex":[0.9992754,0.00005853783,0.0001128728,0.0002936563,0.00006286335,0.0001966974],"domain_scores_gemma":[0.999781,0.00002583674,0.00005152101,0.00004237862,0.000005235607,0.00009409753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008062746,0.00003026531,0.9981359,0.000005635627,0.00004737645,0.000005781632,0.0001577484,0.00002198517,0.0001410798,0.0000529584,0.0006435427,0.0006770595],"study_design_scores_gemma":[0.0006106156,0.0002398394,0.9964674,0.000001440686,0.00002753199,0.00001785541,0.00005402422,0.0005049804,0.00001169524,0.001265215,0.0006992577,0.0001001521],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997053,0.0001148048,0.0001073823,0.00187428,0.0002115225,0.0001582194,0.00007391784,0.00002625547,0.0003806718],"genre_scores_gemma":[0.9984164,0.00005921374,0.0001649765,0.001272583,0.00004995576,0.000008110297,0.00002305324,0.000001665795,0.000004043651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001756963,"threshold_uncertainty_score":0.4842315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009786205243596377,"score_gpt":0.2242899481306643,"score_spread":0.2145037428870679,"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."}}