{"id":"W2161598609","doi":"10.1016/j.mimet.2013.04.008","title":"A simple shotgun proteomics method for rapid bacterial identification","year":2013,"lang":"en","type":"article","venue":"Journal of Microbiological Methods","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Shotgun proteomics; Shotgun; Proteome; Computational biology; Database search engine; Proteomics; Shotgun sequencing; Identification (biology); Bottom-up proteomics; Genome; Bacterial genome size; Biology; Computer science; Bioinformatics; Gene; Genetics; Chemistry; Mass spectrometry; Tandem mass spectrometry; Search engine; Chromatography; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.003231797,0.0001474571,0.0003048897,0.00005547058,0.00008891405,0.0001099529,0.0003215362,0.0002503195,0.000248178],"category_scores_gemma":[0.002883541,0.0001106561,0.0002723896,0.00007837976,0.00006166613,0.00001529292,0.00007500647,0.000135106,0.000008063173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002358954,"about_ca_system_score_gemma":0.00006262059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005490427,"about_ca_topic_score_gemma":0.000001180467,"domain_scores_codex":[0.9978279,0.0008195444,0.0008143304,0.0002753528,0.00004969506,0.0002131058],"domain_scores_gemma":[0.9982185,0.000206323,0.0006212555,0.0002510072,0.0006049176,0.00009798217],"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":[0.0001365718,0.00007460442,0.0001153661,0.00001374291,0.00004146548,1.456407e-7,0.00001796282,0.000004539677,0.951076,0.00002860691,0.002435897,0.0460551],"study_design_scores_gemma":[0.0006245049,0.0005387935,0.006037869,0.000007796069,0.00003408437,0.00006834447,0.00005601202,0.0001227071,0.9467477,0.001626997,0.04396999,0.000165248],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4946402,0.0000770491,0.5041265,0.0002281019,0.000451144,0.0004314778,0.00001679697,0.000006776972,0.00002198879],"genre_scores_gemma":[0.1456545,0.00004048492,0.8530577,0.0002281486,0.0006901876,0.00004664819,0.0001000605,0.00001556549,0.0001666461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3489857,"threshold_uncertainty_score":0.4512424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03658940141034824,"score_gpt":0.3719498417722362,"score_spread":0.3353604403618879,"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."}}