{"id":"W3084390165","doi":"10.3390/life10090185","title":"An Overview of Bioinformatics Tools for DNA Meta-Barcoding Analysis of Microbial Communities of Bioaerosols: Digest for Microbiologists","year":2020,"lang":"en","type":"review","venue":"Life","topic":"Indoor Air Quality and Microbial Exposure","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency; Université Laval; University of Toronto; Institut universitaire de cardiologie et de pneumologie de Québec; Sunnybrook Health Science Centre","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Indoor bioaerosol; Microbial ecology; Ecology; Identification (biology); Bioaerosol; Microbiome; Computer science; Data science; Workflow; Biology; Computational biology; Bioinformatics; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009173428,0.0004096019,0.003956753,0.0001582519,0.00009873121,0.00003452018,0.0009826047,0.0003368123,0.0001808572],"category_scores_gemma":[0.00023787,0.0003220873,0.002475485,0.0007153199,0.0005364186,0.0002465639,0.0002549504,0.0001636077,0.000005177787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006637641,"about_ca_system_score_gemma":0.0001035437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003090269,"about_ca_topic_score_gemma":0.0003573455,"domain_scores_codex":[0.9971613,0.0002848004,0.001890262,0.0002343775,0.0001357777,0.0002934602],"domain_scores_gemma":[0.9964323,0.000943072,0.001834883,0.0006452058,0.00005653543,0.00008802755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003729884,0.001708557,0.0002269826,0.3074522,0.06267854,0.000001426979,0.01475292,0.0009518748,0.03018419,0.00437689,0.005730963,0.5715625],"study_design_scores_gemma":[0.0004545301,0.0005509598,0.00001223044,0.001958295,0.03825413,0.000002494094,0.0006749042,0.0001095825,0.004276222,0.00001786825,0.9530886,0.0006001465],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001254369,0.966055,0.002162043,0.00005556764,0.000172406,0.003035257,0.02709584,0.00003266453,0.0001368564],"genre_scores_gemma":[0.00225107,0.9809036,0.01252814,0.0002349264,0.00004193336,0.0001193257,0.003847888,0.00004249653,0.0000306313],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9473577,"threshold_uncertainty_score":0.9999231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2772756225037591,"score_gpt":0.3797656696668505,"score_spread":0.1024900471630914,"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."}}