{"id":"W4384155152","doi":"10.3389/fmicb.2023.1188872","title":"Targeted metagenomics using bait-capture to detect antibiotic resistance genes in retail meat and seafood","year":2023,"lang":"en","type":"article","venue":"Frontiers in Microbiology","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency; Health Canada","funders":"Government of Canada","keywords":"Metagenomics; Resistome; Biology; Microbiome; Shotgun; Gene; Antibiotic resistance; Plasmid; Shotgun sequencing; Computational biology; Biotechnology; Genetics; Genome; Bacteria; Integron","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":[0.0004051508,0.0006844524,0.0007337669,0.0007239456,0.0003588702,0.000646629,0.0002458896,0.0005465132,0.0004107965],"category_scores_gemma":[0.000491592,0.0002892599,0.0006513175,0.0004925306,0.0002481243,0.0003592596,0.0006212564,0.0004024154,0.0001960015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003211499,"about_ca_system_score_gemma":0.000306159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0016156,"about_ca_topic_score_gemma":0.004184017,"domain_scores_codex":[0.9995524,0.00005955851,0.00002268443,0.000173164,0.0001379416,0.00005416042],"domain_scores_gemma":[0.9998293,0.00005513751,0.00004295567,0.00001459297,0.00003852843,0.00001951067],"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":[0.0000570515,0.00002523813,0.006147314,0.00006677494,0.00003470772,0.00002147127,0.00005869938,0.0001254127,0.9904489,0.00003347973,0.00002128515,0.002959707],"study_design_scores_gemma":[0.00001654719,0.001055222,0.1914735,0.00005154967,0.0001974993,0.000616492,0.0004673988,0.00676872,0.7972111,0.0002948655,0.001806561,0.00004056159],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761862,0.001129351,0.02057323,0.0000995862,0.00002275456,0.00009083387,0.001131981,0.0001152292,0.0006508105],"genre_scores_gemma":[0.9517319,0.001178786,0.04299522,0.0002076843,0.00001509234,0.0002012489,0.002353377,0.00005923853,0.00125742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0016156,"threshold_uncertainty_score":0.003212333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01248841968430269,"score_gpt":0.2237831172664588,"score_spread":0.2112946975821561,"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."}}