{"id":"W4391449885","doi":"10.1128/mra.01149-23","title":"Complete genome sequence of multidrug-resistant <i>Enterobacter roggenkampii</i> 0-E","year":2024,"lang":"en","type":"article","venue":"Microbiology Resource Announcements","topic":"Antibiotic Resistance in Bacteria","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs; Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"Canadian Institutes of Health Research; Government of Canada","keywords":"Enterobacter cloacae; Genome; Multiple drug resistance; Whole genome sequencing; Enterobacter; Biology; Nanopore sequencing; Gene; Genetics; Computational biology; Drug resistance; Enterobacteriaceae; Escherichia coli","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002629783,0.0002952158,0.0003121169,0.00008973701,0.00008322886,0.00004339884,0.0004782566,0.0002211318,0.0000833615],"category_scores_gemma":[0.00002312141,0.0002658315,0.0001706165,0.0001475797,0.0004305078,0.00000787624,0.0002549356,0.0001430503,0.00008854693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004789876,"about_ca_system_score_gemma":0.00007564053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001760662,"about_ca_topic_score_gemma":0.00001591602,"domain_scores_codex":[0.9981111,0.0001206732,0.0005089326,0.0006910395,0.00009064469,0.0004775909],"domain_scores_gemma":[0.9991048,0.00002006602,0.0001362088,0.0005773626,0.00009047172,0.00007109596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002070797,0.00006047688,0.004325886,0.00009298274,0.0002252604,0.00002199643,0.00008156832,0.00001305199,0.9899229,0.00002560422,0.004873756,0.0001494111],"study_design_scores_gemma":[0.0003781022,0.0002861704,0.0005467467,0.0001113966,0.00003504609,0.00005859045,0.00007122095,0.00001866146,0.03021292,0.0000100752,0.9679922,0.0002789075],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866477,0.002075325,0.0005764082,0.0001338342,0.0004528666,0.0003371101,0.008350517,0.00004290373,0.00138338],"genre_scores_gemma":[0.9767212,0.0002084797,0.0009875237,0.0006324895,0.000236893,0.00001611423,0.0156879,0.00006174641,0.005447689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9631184,"threshold_uncertainty_score":0.9999794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750351120624959,"score_gpt":0.2549689964540826,"score_spread":0.237465485247833,"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."}}