{"id":"W4362556144","doi":"10.1111/geb.13664","title":"Drivers of the microbial metabolic quotient across global grasslands","year":2023,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Fundação para a Ciência e a Tecnologia; Swiss Federal Institute for Forest, Snow and Landscape Research; Deutsche Forschungsgemeinschaft","keywords":"Biomass (ecology); Respiratory quotient; Nutrient; Herbivore; Ecology; Incubation; Soil water; Animal science; Biology; Chemistry; Environmental 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":[0.000482352,0.0002621398,0.0002616746,0.0005394305,0.0002024326,0.0006132456,0.000163439,0.0002895432,0.0007688294],"category_scores_gemma":[0.0007446447,0.0001933525,0.0002692141,0.0006230747,0.0003664903,0.000361932,0.0005220999,0.0001719704,0.0000834194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004308178,"about_ca_system_score_gemma":0.0002104293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01006777,"about_ca_topic_score_gemma":0.01474165,"domain_scores_codex":[0.9997364,0.00007130607,0.00001603852,0.0001107868,0.00002776389,0.00003768266],"domain_scores_gemma":[0.99958,0.0000883409,0.0001766148,0.00003655465,0.00006434051,0.00005417369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001509917,0.00003913103,0.9577798,0.00008904206,0.0003608302,0.00008981064,0.000367656,0.002254809,0.03024852,0.0002905658,0.000177144,0.008151723],"study_design_scores_gemma":[0.00000158533,0.00001309867,0.9989302,0.000001837368,0.00001097171,0.00001144637,0.00009936387,0.0006401891,0.0001093279,0.00005608996,0.0001226966,0.000003309555],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990231,0.0001062277,0.000206241,0.0000318296,0.000001207222,0.000003312567,0.0003473065,0.00000853776,0.0002722238],"genre_scores_gemma":[0.9995653,0.00003396366,0.0001553854,0.00001172918,0.000001433781,0.000003883719,0.0001847052,0.000002666816,0.00004101213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01006777,"threshold_uncertainty_score":0.02001834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006486536287181009,"score_gpt":0.2155869316828985,"score_spread":0.2091003953957175,"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."}}