{"id":"W2102718331","doi":"10.1186/1756-0381-7-25","title":"Updating microbial genomic sequences: improving accuracy & innovation","year":2014,"lang":"en","type":"article","venue":"BioData Mining","topic":"Vibrio bacteria research studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McMaster University","keywords":"Sanger sequencing; Biology; Genome; Whole genome sequencing; Computational biology; Salmonella enterica; Biovar; Genetics; DNA sequencing; Caulobacter crescentus; Salmonella; Gene; Bacterial protein; 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":[0.02190073,0.001067205,0.001277305,0.003529945,0.0007652704,0.004448069,0.003421153,0.002112426,0.002457598],"category_scores_gemma":[0.07841668,0.0009130259,0.000990984,0.005029032,0.001094919,0.006034846,0.002622802,0.00203291,0.003566555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218134,"about_ca_system_score_gemma":0.002209012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001553344,"about_ca_topic_score_gemma":0.001940279,"domain_scores_codex":[0.9809625,0.005619484,0.001787115,0.002913796,0.008271631,0.0004456358],"domain_scores_gemma":[0.9483619,0.01995257,0.004233462,0.01230311,0.01458784,0.000561013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004085229,0.0001737083,0.02149896,0.001134096,0.0001670644,0.0003221512,0.001145597,0.01609991,0.07974458,0.009016027,0.005812893,0.8644766],"study_design_scores_gemma":[0.0001824676,0.0007591658,0.02724026,0.001021279,0.0005213632,0.00279953,0.001414994,0.271931,0.3957653,0.05961592,0.2383621,0.0003867048],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08496558,0.004928005,0.8877417,0.004835231,0.0008460422,0.0003186144,0.002115476,0.007130165,0.007119163],"genre_scores_gemma":[0.1242836,0.001961703,0.8676059,0.0008640637,0.000246646,0.0001290344,0.002386027,0.0007188593,0.001804159],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02190073,"threshold_uncertainty_score":0.1158235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03551480171943568,"score_gpt":0.3013115225258921,"score_spread":0.2657967208064564,"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."}}