{"id":"W2775051405","doi":"10.1101/221325","title":"Tn-Core: context-specific reconstruction of core metabolic models using Tn-seq data","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Computational biology; Data integration; Computer science; RNA-Seq; Toolbox; Metabolic engineering; Core (optical fiber); Software; Data mining; Biology; Programming language; Gene; Genetics; Transcriptome","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":[0.0007161455,0.000587677,0.000771313,0.0002016371,0.0002031544,0.0001232049,0.001293493,0.0007927694,0.00001275671],"category_scores_gemma":[0.0002222401,0.0006341574,0.0001897176,0.0001681135,0.0002834435,0.00004057488,0.001111339,0.0005201917,0.000005295266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004641004,"about_ca_system_score_gemma":0.0005044705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000140584,"about_ca_topic_score_gemma":0.000006129625,"domain_scores_codex":[0.9970368,0.00006747145,0.000684899,0.001497032,0.0002584053,0.0004553516],"domain_scores_gemma":[0.9941368,0.000005294854,0.0008400726,0.004203345,0.0006472709,0.0001672125],"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":[0.00005567353,0.00006450287,0.0003633728,0.0001994159,0.0002527018,0.00000345307,0.000003809435,0.0008085815,0.9971777,0.000454362,0.0005232863,0.00009310102],"study_design_scores_gemma":[0.000546931,0.00004023636,0.00172869,0.0003525024,0.0002449548,6.281086e-7,0.000004104189,0.003518745,0.967227,0.00001084419,0.02547728,0.0008480338],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9651579,0.01810675,0.0109987,0.00004131494,0.003854712,0.0005637018,0.001176877,0.00009247653,0.000007540672],"genre_scores_gemma":[0.9808747,0.005522007,0.01128922,0.00002683029,0.002101289,0.00002487494,0.00001678416,0.0001294997,0.00001483526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02995069,"threshold_uncertainty_score":0.999611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.063870996646413,"score_gpt":0.2576926689734479,"score_spread":0.1938216723270349,"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."}}