{"id":"W3189270049","doi":"10.1101/2021.08.06.455440","title":"Diversity of Antibiotic Resistance genes and Transfer Elements-Quantitative Monitoring (DARTE-QM): a method for detection of antimicrobial resistance in environmental samples","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Institute of Food and Agriculture; U.S. Department of Agriculture","keywords":"Antibiotic resistance; Antibiotics; Biology; Environmental DNA; Resistance (ecology); Gene; Metagenomics; Biotechnology; Computational biology; Genetics; Ecology; Biodiversity","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":[0.001779742,0.0006173239,0.000655023,0.001043265,0.0003445637,0.0007434796,0.0006086241,0.0008426849,0.0009022325],"category_scores_gemma":[0.001928358,0.0003674985,0.0003257888,0.0006439832,0.0006392206,0.0006077781,0.0009522543,0.001014496,0.0003922629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002367341,"about_ca_system_score_gemma":0.0002677127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003580449,"about_ca_topic_score_gemma":0.0006760149,"domain_scores_codex":[0.9982647,0.0004483649,0.00008096313,0.0004734149,0.000619716,0.0001128217],"domain_scores_gemma":[0.9990024,0.0004211817,0.0002269166,0.0001295142,0.000124641,0.00009533418],"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":[0.00004839469,0.00005125996,0.003510029,0.00006366861,0.00001917797,0.0000196716,0.00007183724,0.0002993444,0.9860455,0.0002618738,0.0001861208,0.009423081],"study_design_scores_gemma":[0.00002689309,0.0002246823,0.01412859,0.00001603004,0.00002361787,0.0002300355,0.00006041857,0.01234444,0.9685017,0.0003846953,0.004019927,0.00003894909],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5221761,0.001146705,0.4669906,0.0005535314,0.0001144169,0.0002789384,0.003656982,0.002677154,0.002405613],"genre_scores_gemma":[0.5498807,0.0006341322,0.4434534,0.000435931,0.00004824379,0.0005012929,0.001965353,0.0001894874,0.002891365],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001779742,"threshold_uncertainty_score":0.009412229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02991744444934499,"score_gpt":0.2659294079337485,"score_spread":0.2360119634844035,"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."}}