{"id":"W4280525003","doi":"10.1099/mgen.0.000818","title":"Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks","year":2022,"lang":"en","type":"article","venue":"Microbial Genomics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Genome Canada; Cisco Systems; Simon Fraser University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Genome; Bacterial genome size; Phylogenetic tree; Computational biology; Biology; ENCODE; Visualization; Consistency (knowledge bases); Data mining; Genetics; Computer science; Gene; Artificial intelligence","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":[0.001919096,0.001048061,0.000920371,0.003229306,0.0007615642,0.00162872,0.001073139,0.0009292401,0.005190971],"category_scores_gemma":[0.005567279,0.0005736226,0.001348644,0.002561241,0.0002731135,0.001761707,0.002302138,0.001081683,0.001941113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000613189,"about_ca_system_score_gemma":0.001043167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004418993,"about_ca_topic_score_gemma":0.004596156,"domain_scores_codex":[0.9993262,0.0001237657,0.00005076088,0.0002658782,0.0001572978,0.00007612774],"domain_scores_gemma":[0.9985389,0.0006911681,0.0002219974,0.0001692101,0.0002288369,0.0001500088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0033293,0.0008184126,0.1446888,0.002592535,0.001320169,0.001578713,0.003029169,0.08872129,0.2739326,0.01341755,0.05281187,0.4137596],"study_design_scores_gemma":[0.0002176553,0.0004085994,0.09065595,0.0003215271,0.0004475441,0.0005965846,0.001168502,0.7743832,0.06997272,0.0210347,0.04053488,0.0002580989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4535837,0.001555062,0.4390044,0.0009649186,0.0002626849,0.0003150903,0.03035969,0.06576969,0.008184736],"genre_scores_gemma":[0.4770858,0.0006344176,0.4744165,0.0002170448,0.00006702499,0.0002889867,0.04077691,0.005059987,0.001453369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005190971,"threshold_uncertainty_score":0.01736552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01258693583036913,"score_gpt":0.2064548125217647,"score_spread":0.1938678766913955,"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."}}