{"id":"W4392750848","doi":"10.1038/s41598-024-56616-y","title":"Divergent bacterial landscapes: unraveling geographically driven microbiomes in Atlantic cod","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Aquaculture disease management and microbiota","field":"Immunology and Microbiology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski; Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Fisheries and Oceans Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Microbiome; Biology; Halibut; Population; Bacteroidetes; Pyrosequencing; Ecology; Acidobacteria; Proteobacteria; Zoology; Genetics; Fishery; 16S ribosomal RNA; Fish <Actinopterygii>; Bacteria","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003082702,0.0001787811,0.0003306539,0.0007251723,0.0004205297,0.0006561746,0.0001979015,0.0002502261,0.0003753545],"category_scores_gemma":[0.0005559098,0.0001633461,0.0002113361,0.000668899,0.0003157062,0.00026792,0.0007860494,0.0002586818,0.00009751852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003072295,"about_ca_system_score_gemma":0.0004002208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01382913,"about_ca_topic_score_gemma":0.03410795,"domain_scores_codex":[0.9997923,0.00003681932,0.00001773665,0.00007732362,0.00003193194,0.00004387891],"domain_scores_gemma":[0.999724,0.00004233694,0.0001029742,0.00002729352,0.00005348075,0.00004986937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001254239,0.00001735707,0.9490077,0.00003723576,0.00005503519,0.0001013384,0.0008313604,0.0001703559,0.04334433,0.00005957469,0.00004567279,0.006204389],"study_design_scores_gemma":[0.000001272896,0.00003283406,0.9980441,0.000008743887,0.00001273984,0.0000932523,0.0007426845,0.0003740754,0.0004416401,0.00005232442,0.000192118,0.000004130556],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991811,0.0001159629,0.0003699573,0.00001155348,0.000001299919,0.000004063195,0.0001457876,0.000001673429,0.000168504],"genre_scores_gemma":[0.9986882,0.00009365383,0.0008162072,0.00002835699,0.000002922924,0.000008305523,0.0002538241,0.0000034078,0.0001051239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01382913,"threshold_uncertainty_score":0.02749729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006313700271594019,"score_gpt":0.2181724791148035,"score_spread":0.2118587788432094,"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."}}