{"id":"W2805844312","doi":"10.1016/j.nmni.2018.05.007","title":"Completion of genome of Aeromonas salmonicida subsp. salmonicida 01-B526 reveals how sequencing technologies can influence sequence quality and result interpretations","year":2018,"lang":"en","type":"article","venue":"New Microbes and New Infections","topic":"Aquaculture disease management and microbiota","field":"Immunology and Microbiology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Institut Universitaire de Cardiologie et de Pneumologie de Québec; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aeromonas salmonicida; Biology; Whole genome sequencing; Pyrosequencing; Genome; Genetics; Strain (injury); Pathogen; Microbiology; Sequence (biology); Sequence assembly; Computational biology; Bacteria; Gene; Transcriptome","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":[],"consensus_categories":[],"category_scores_codex":[0.0002078387,0.0002445808,0.0004302317,0.0002193788,0.0003007033,0.0000375201,0.0001963396,0.0002398564,0.00005043898],"category_scores_gemma":[0.0001995814,0.0002260589,0.00009112764,0.0003701017,0.0009014599,0.0001932808,0.0002188478,0.0002284482,0.0000097919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009220844,"about_ca_system_score_gemma":0.0001436209,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006951647,"about_ca_topic_score_gemma":0.003218725,"domain_scores_codex":[0.998648,0.0001168893,0.0004878142,0.000408918,0.00003990752,0.0002984405],"domain_scores_gemma":[0.9988936,0.0001125485,0.0003847237,0.0003550173,0.0002073813,0.00004669705],"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":[0.00004589247,0.0000466628,0.009986404,0.00008261655,0.0001609481,7.537322e-7,0.001584311,0.00000818232,0.9828042,0.002451454,0.001388437,0.0014401],"study_design_scores_gemma":[0.003173471,0.001371048,0.2390884,0.0007776782,0.0006455895,0.000241407,0.006598003,0.000009560268,0.7198853,0.004332642,0.02283075,0.001046105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993262,0.003260456,0.001078754,0.001226685,0.0001271975,0.0003835057,0.0001783423,0.0001336606,0.0003493844],"genre_scores_gemma":[0.997578,0.0006390451,0.0005752018,0.0001129281,0.00002383606,0.000007890878,0.0001093235,0.00001454224,0.0009392952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2629189,"threshold_uncertainty_score":0.9996611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04235095998156341,"score_gpt":0.290495591346717,"score_spread":0.2481446313651536,"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."}}