{"id":"W2297505554","doi":"10.3389/fmicb.2016.00068","title":"Using “Omics” and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases","year":2016,"lang":"en","type":"review","venue":"Frontiers in Microbiology","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Deutsche Forschungsgemeinschaft; Deutscher Akademischer Austauschdienst; National Science Foundation","keywords":"Biology; Chytridiomycosis; Metagenomics; Computational biology; Microbiome; In silico; Biotechnology; Bioinformatics; Microbiology; Pathogen; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003482868,0.001794634,0.001972447,0.003295701,0.0007385908,0.004512306,0.0008430423,0.001418775,0.001613908],"category_scores_gemma":[0.003216775,0.0005373775,0.002062958,0.002309303,0.000611227,0.002340442,0.0015285,0.001589646,0.001036548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032972,"about_ca_system_score_gemma":0.002284821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002493903,"about_ca_topic_score_gemma":0.005769755,"domain_scores_codex":[0.9986163,0.0004631632,0.0001711763,0.0002964589,0.0003471894,0.0001056793],"domain_scores_gemma":[0.9983522,0.000418993,0.0003297685,0.0001901403,0.0005516385,0.0001573096],"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":[0.0009429872,0.0006831373,0.06988473,0.004546516,0.00148728,0.0005175574,0.0005961505,0.01145345,0.6387896,0.003846979,0.009132,0.2581196],"study_design_scores_gemma":[0.0002830874,0.00201266,0.2758345,0.003405566,0.003129158,0.0010453,0.004584638,0.0992837,0.3474925,0.04074431,0.2215558,0.0006287762],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.4294901,0.06420021,0.4288107,0.01995607,0.001842854,0.002142364,0.03272468,0.004202072,0.01663099],"genre_scores_gemma":[0.3989124,0.02720818,0.5421526,0.007451598,0.0004669256,0.001079108,0.01868316,0.0005371604,0.003508921],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004512306,"threshold_uncertainty_score":0.01841938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06559642701546813,"score_gpt":0.3025563235199275,"score_spread":0.2369598965044594,"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."}}