{"id":"W4383711201","doi":"10.1111/gcb.16840","title":"Temperature‐mediated microbial carbon utilization in China's lakes","year":2023,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"National Natural Science Foundation of China","keywords":"Carbon fibers; Environmental science; Carbon cycle; Microbial population biology; Climate change; Ecology; Global warming; Dissolved organic carbon; Carbon sequestration; Abundance (ecology); Environmental chemistry; Biology; Chemistry; Ecosystem; Carbon dioxide; Bacteria","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001914462,0.0001291288,0.0001780669,0.00004705615,0.0000836219,0.000004627584,0.0002524931,0.0003189252,0.0009526847],"category_scores_gemma":[0.00005756396,0.0001215626,0.00002997295,0.0007203657,0.0002156182,0.00004384387,0.0002917334,0.0001743654,0.0006669427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001207601,"about_ca_system_score_gemma":0.000008643285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002791256,"about_ca_topic_score_gemma":0.01586301,"domain_scores_codex":[0.9988774,0.0003087968,0.000147007,0.0002518111,0.00002996124,0.0003850172],"domain_scores_gemma":[0.9996884,0.00003860403,0.00004420502,0.0001806977,0.000004263531,0.00004384531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001016469,0.0001053913,0.8493048,0.000008931374,0.00001135923,0.00002227157,0.0006667417,0.00001183925,0.1387385,0.0003850145,0.008831291,0.001812173],"study_design_scores_gemma":[0.0004017439,0.0001442453,0.9811555,0.000005389591,0.000003733404,0.00001021463,0.00003545534,0.0001636868,0.000200627,0.001543836,0.01616239,0.0001732257],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946859,0.00005853508,4.767403e-7,0.0006472691,0.000512684,0.0002076062,0.00009616868,0.00009549591,0.003695842],"genre_scores_gemma":[0.9980506,0.0001124275,0.000009863827,0.0007180674,0.00009107299,0.00002713148,0.0009134274,0.000005535896,0.00007182994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1385379,"threshold_uncertainty_score":0.9999606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03483760658623514,"score_gpt":0.274419239023986,"score_spread":0.2395816324377509,"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."}}