{"id":"W4414780972","doi":"10.1111/1758-2229.70187","title":"Bacterial <scp>16S</scp> Ribosomal Gene Fingerprints as a Tool to Diagnose and Mitigate Fish Larvae Gut Dysbiosis","year":2025,"lang":"en","type":"article","venue":"Environmental Microbiology Reports","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Fundação para a Ciência e a Tecnologia; Horizon 2020 Framework Programme; European Commission","keywords":"Dysbiosis; Firmicutes; Gammaproteobacteria; Gut flora; Proteobacteria; Microbiome; Larva; Vibrio","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002059104,0.0003355384,0.000341619,0.00008119309,0.0001974595,0.00003632053,0.0001566761,0.000421243,0.0001120158],"category_scores_gemma":[0.0001294301,0.000351195,0.0001177359,0.00006288164,0.0002385472,0.000007651018,0.000613845,0.0001624681,0.00006580319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005594248,"about_ca_system_score_gemma":0.00008333907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003668179,"about_ca_topic_score_gemma":0.00004784004,"domain_scores_codex":[0.9978453,0.00009822534,0.0005020925,0.0009518281,0.00004361739,0.0005589385],"domain_scores_gemma":[0.9991511,0.00003188143,0.0001699611,0.0004708063,0.00001053414,0.0001657311],"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":[0.00003215001,0.0001338382,0.04342826,0.00001800518,0.00009887307,0.000131562,0.00006409964,0.000001972785,0.9458727,0.000003534491,0.009704258,0.0005106929],"study_design_scores_gemma":[0.0004414257,0.0002266721,0.09881414,0.00002488831,0.000036916,0.001015279,0.00003446573,1.95693e-7,0.699619,0.00002788463,0.1996101,0.000149035],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972897,0.0004864459,0.00002813459,0.0003921835,0.0006670995,0.0005493102,0.0002510124,0.00002242003,0.0003137283],"genre_scores_gemma":[0.9899604,0.0004634795,0.0006839228,0.003852632,0.0001748981,0.00006799175,0.001034547,0.00003203968,0.003730126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2462537,"threshold_uncertainty_score":0.999894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002816192484431179,"score_gpt":0.2098145271878971,"score_spread":0.2069983347034659,"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."}}