{"id":"W2742786000","doi":"10.1101/172858","title":"MentaLiST – A fast MLST caller for large MLST schemes","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control; Simon Fraser University","funders":"","keywords":"Multilocus sequence typing; Housekeeping gene; Genotyping; Typing; Computer science; Biology; Computational biology; Genetics; Gene; Genotype","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004065636,0.0006359101,0.0005630633,0.0000803571,0.0005279226,0.0002406303,0.0008682563,0.0006365693,0.00001501759],"category_scores_gemma":[0.0001829713,0.0006764701,0.0003351773,0.0000501615,0.0001500062,0.000002512771,0.001381978,0.0002769227,0.00001885687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007063502,"about_ca_system_score_gemma":0.0003807815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003213768,"about_ca_topic_score_gemma":0.00002364841,"domain_scores_codex":[0.9973903,0.00004768211,0.0004252411,0.001215578,0.000206809,0.0007144009],"domain_scores_gemma":[0.9970702,0.00001593097,0.0004461315,0.001806026,0.0004398363,0.0002219048],"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":[0.00006671371,0.0001511835,0.00588769,0.0002518462,0.0005743767,0.000006959456,0.000009341237,0.000009524972,0.988922,0.0004194188,0.003697462,0.000003487941],"study_design_scores_gemma":[0.001398842,0.000166771,0.03411139,0.0001425265,0.0001931326,3.023127e-8,0.00001020768,0.00009792401,0.5998781,0.000006794085,0.3627686,0.001225682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842507,0.006676632,0.002235189,0.0003254819,0.002067972,0.001266061,0.003055522,0.00004164514,0.00008076175],"genre_scores_gemma":[0.9924804,0.001063789,0.004236388,0.0002801348,0.001308026,0.0003780721,0.000007484145,0.0001582618,0.00008745827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3890439,"threshold_uncertainty_score":0.9995686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607216877287984,"score_gpt":0.2454890424562749,"score_spread":0.2294168736833951,"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."}}