{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005975848,0.001279557,0.0007320641,0.001091771,0.0004054914,0.001153327,0.0005348996,0.0008561727,0.003569534],"category_scores_gemma":[0.0007941024,0.000436294,0.0007523076,0.001912243,0.0002323807,0.0007982163,0.0006302609,0.0007360086,0.001910535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005097893,"about_ca_system_score_gemma":0.000458621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001177389,"about_ca_topic_score_gemma":0.002153337,"domain_scores_codex":[0.9995543,0.00006670491,0.00001617594,0.0001552917,0.0001642692,0.00004313542],"domain_scores_gemma":[0.9998193,0.0000480004,0.00003198785,0.00004829376,0.00002654112,0.0000258166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001025827,0.0001857324,0.003501944,0.0008364788,0.0003032202,0.0001632635,0.00007446657,0.005719665,0.9078582,0.004488608,0.01801839,0.05782417],"study_design_scores_gemma":[0.0002011665,0.0002593982,0.04078785,0.00006159351,0.0002763741,0.0008717733,0.00006960933,0.08105879,0.8003219,0.01548154,0.06050482,0.0001050669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.332405,0.007087773,0.4450846,0.002899042,0.0006387059,0.0003141295,0.179642,0.01873963,0.01318921],"genre_scores_gemma":[0.5712177,0.003603139,0.2889126,0.0005254808,0.0002472803,0.0005791402,0.1213068,0.001936515,0.01167152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003569534,"threshold_uncertainty_score":0.01194131,"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."}}