{"id":"W4308054597","doi":"10.1128/jcm.01032-22","title":"A Practical Workflow for the Identification of Aspergillus, Fusarium, Mucorales by MALDI-TOF MS: Database, Medium, and Incubation Optimization","year":2022,"lang":"en","type":"article","venue":"Journal of Clinical Microbiology","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Hospital for Sick Children; Public Health Ontario; Trillium Health Centre; University of Toronto","funders":"","keywords":"Mucorales; Aspergillus; Fusarium; Identification (biology); Biology; Aspergillus fumigatus; Microbiology; Agar; Database; Botany; Mucormycosis; Medicine; Bacteria; Genetics; Computer science","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":[0.003439241,0.00008519683,0.0002505794,0.00004079452,0.0001461065,0.00002227314,0.0002114784,0.0001239625,0.00005653606],"category_scores_gemma":[0.004208844,0.00006670323,0.0001260063,0.0000814545,0.0002079814,0.00001603537,0.00014846,0.0002179842,4.177945e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001269205,"about_ca_system_score_gemma":0.0001387083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004823913,"about_ca_topic_score_gemma":0.000009771287,"domain_scores_codex":[0.9977332,0.0005068656,0.001365221,0.0002200298,0.0000646366,0.0001100129],"domain_scores_gemma":[0.997326,0.0006613884,0.001369715,0.0002316569,0.000363166,0.00004812951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00059867,0.0002328877,0.003480216,0.00001914555,0.00009081292,3.448159e-7,0.00002787291,0.0004769092,0.9848268,0.00007979383,0.00799447,0.002172069],"study_design_scores_gemma":[0.01375204,0.00911926,0.03814204,0.00009004476,0.001329717,0.001507432,0.002098755,0.02223472,0.2856654,0.001209137,0.6236076,0.001243812],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8265023,0.0008696091,0.1641349,0.0060677,0.001514955,0.0004516291,0.000444236,0.000005460018,0.000009199621],"genre_scores_gemma":[0.9849492,0.0004853484,0.01301041,0.000338778,0.0003290287,0.00001506081,0.0007634945,0.00001209178,0.00009659013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6991614,"threshold_uncertainty_score":0.5038683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04974612652984722,"score_gpt":0.3688904171915248,"score_spread":0.3191442906616775,"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."}}