{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001719118,0.0002485117,0.0002611965,0.0004999526,0.000408544,0.000361297,0.0001318338,0.0001831812,0.0003440026],"category_scores_gemma":[0.0001997792,0.000162821,0.0002975931,0.0005341262,0.0002303696,0.0003585932,0.0004166355,0.0001195273,0.00004523581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005465215,"about_ca_system_score_gemma":0.0004642915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01831628,"about_ca_topic_score_gemma":0.02241989,"domain_scores_codex":[0.9998496,0.00002036725,0.00001301293,0.00004612649,0.00002937256,0.00004161173],"domain_scores_gemma":[0.9998835,0.00001162241,0.00003826949,0.000006580665,0.00003454713,0.00002538417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004027109,0.0000640744,0.6754621,0.0001290463,0.0001469933,0.0001639995,0.001203788,0.001378461,0.3106363,0.0001760442,0.0001401631,0.01009625],"study_design_scores_gemma":[0.000003094252,0.00003988453,0.9951,0.000002545177,0.00001870857,0.00002738934,0.0002401153,0.000937505,0.00347346,0.0000347895,0.0001153727,0.000007078863],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997227,0.00005255045,0.00004784878,0.000006879065,7.674998e-7,0.000001462481,0.00006095861,0.000001253838,0.0001057272],"genre_scores_gemma":[0.9996707,0.00004368107,0.00007265984,0.000007301254,0.000001257668,0.000003828465,0.0001069725,8.321741e-7,0.00009265664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01831628,"threshold_uncertainty_score":0.03641933,"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."}}