{"id":"W3205706571","doi":"10.21203/rs.3.rs-965097/v1","title":"METABOLIC: High-Throughput Profiling of Microbial Genomes for Functional Traits, Metabolism, Biogeochemistry, and Community-scale Functional Networks","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Division of Graduate Education; University of Wisconsin-Madison; National Science Foundation","keywords":"Metagenomics; Biogeochemical cycle; Biogeochemistry; Microbiome; Genome; Biology; Metabolic network; Microbial ecology; Computational biology; Cyberinfrastructure; Microbial metabolism; Genomics; Microbial population biology; Workflow; Metabolic pathway; Systems biology; Ecology; Bioinformatics; Data science; Genetics; Computer science; Database; Gene; Bacteria","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001870095,0.0003839799,0.0006030174,0.0001825733,0.0004311044,0.0001126304,0.0003070956,0.0007110508,0.00003227104],"category_scores_gemma":[0.0003306625,0.0003908241,0.0003176068,0.000269135,0.0003317425,0.000008997177,0.001003401,0.001446451,6.521837e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002987051,"about_ca_system_score_gemma":0.0004170392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002403245,"about_ca_topic_score_gemma":0.00006910955,"domain_scores_codex":[0.9972386,0.0005151266,0.0004888796,0.0008210787,0.0003750356,0.0005612826],"domain_scores_gemma":[0.9976838,0.00006475328,0.0001663288,0.0006901133,0.001247168,0.0001478678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003222433,0.0002487538,0.0001666575,0.001160189,0.000378487,5.456528e-7,0.00008037653,0.004508421,0.989166,0.0001171628,0.001634551,0.002216631],"study_design_scores_gemma":[0.0009203358,0.0001632861,0.006017495,0.000171554,0.000141474,0.00003423422,0.000470074,0.0002441905,0.9652808,0.00009784998,0.02601062,0.0004480392],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9506549,0.02907483,0.01718019,0.0001952357,0.0009139249,0.0008917361,0.001023373,0.00003094675,0.00003487639],"genre_scores_gemma":[0.9729365,0.00491585,0.007742589,0.00003240047,0.00366881,0.0002711586,0.009973856,0.00007865793,0.0003801476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02437607,"threshold_uncertainty_score":0.9998544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03563175736550016,"score_gpt":0.2993077420642854,"score_spread":0.2636759846987852,"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."}}