{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003622362,0.0007673746,0.0008569252,0.001923022,0.0004794743,0.001246069,0.0006542325,0.0006343482,0.001801926],"category_scores_gemma":[0.00398167,0.0005212282,0.003362895,0.001341752,0.0005438977,0.0006948522,0.001477383,0.0005155447,0.0001219558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003509083,"about_ca_system_score_gemma":0.0004836404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003570915,"about_ca_topic_score_gemma":0.003708127,"domain_scores_codex":[0.9976527,0.001135901,0.000154792,0.0007137402,0.0001884295,0.0001544305],"domain_scores_gemma":[0.9951844,0.002297613,0.001217513,0.0005939106,0.0003263537,0.0003801899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001105922,0.00009784839,0.91264,0.002082369,0.03559201,0.0002271472,0.0002549724,0.003350994,0.0181596,0.0006229705,0.0004556832,0.02541046],"study_design_scores_gemma":[0.00004648982,0.0003760902,0.9775668,0.0002325706,0.01174332,0.0001807436,0.0003196977,0.005809438,0.001896587,0.0008954855,0.0009058209,0.00002700238],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687389,0.02473775,0.00453301,0.0002551717,0.00004699025,0.0000220753,0.0006286002,0.0000821252,0.0009553916],"genre_scores_gemma":[0.9973996,0.001261565,0.0009542713,0.00005719428,0.00001362251,0.000007642073,0.0001815603,0.000008961915,0.0001156244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003622362,"threshold_uncertainty_score":0.01915717,"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."}}