{"id":"W4361197586","doi":"10.1186/s40793-023-00482-0","title":"Exploiting a targeted resistome sequencing approach in assessing antimicrobial resistance in retail foods","year":2023,"lang":"en","type":"article","venue":"Environmental Microbiome","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency","funders":"Agriculture and Agri-Food Canada; Canadian Food Inspection Agency","keywords":"Resistome; Metagenomics; Biology; Computational biology; Biotechnology; Antibiotic resistance; Shotgun sequencing; DNA sequencing; Plasmid; Genetics; Gene; Bacteria; Mobile genetic elements","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":[0.0007746599,0.0008370776,0.0005823699,0.001264173,0.0003075709,0.0006959759,0.0004680271,0.001041375,0.0007414377],"category_scores_gemma":[0.001036466,0.0003009846,0.0006656186,0.000563497,0.0003494119,0.0004981706,0.0007963178,0.0005554421,0.0005803801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002136086,"about_ca_system_score_gemma":0.0003278452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000573574,"about_ca_topic_score_gemma":0.001347558,"domain_scores_codex":[0.9990895,0.0001657971,0.00005822625,0.0003164673,0.0002948241,0.00007526705],"domain_scores_gemma":[0.9995614,0.0001263532,0.0001150918,0.00003266375,0.0001324101,0.00003205337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009187688,0.00005745791,0.004037914,0.0001719092,0.00003303274,0.0001044333,0.00009105224,0.0006309273,0.9835646,0.00007429362,0.00008047886,0.01106199],"study_design_scores_gemma":[0.0000201637,0.001117678,0.03510032,0.0001189941,0.0001666508,0.001419772,0.0003843265,0.01866455,0.9384047,0.0007078249,0.003816008,0.00007908627],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8477169,0.001755987,0.1443335,0.000319257,0.00007556089,0.0004206477,0.002380115,0.00085437,0.002143567],"genre_scores_gemma":[0.7983675,0.001949192,0.1946461,0.0004471417,0.00004842832,0.0004527596,0.002315852,0.0001382984,0.001634716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001264173,"threshold_uncertainty_score":0.004096866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03633560597784589,"score_gpt":0.2647477726322374,"score_spread":0.2284121666543915,"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."}}