{"id":"W4390858353","doi":"10.1093/ismejo/wrae005","title":"<i>Escherichia coli</i> CRISPR arrays from early life fecal samples preferentially target prophages","year":2024,"lang":"en","type":"article","venue":"The ISME Journal","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Det Sundhedsvidenskabelige Fakultet, Københavns Universitet; Natural Sciences and Engineering Research Council of Canada; Compute Canada; Canadian Institutes of Health Research; Faculty of Health and Medical Sciences, University of Western Australia; Alliance de recherche numérique du Canada; Canada First Research Excellence Fund; Novo Nordisk Fonden; Fonds de recherche du Québec – Nature et technologies; Danish Agency for Science and Higher Education; Copenhagen Graduate School for Nanoscience and Nanotechnology; Novo Nordisk","keywords":"Biology; Prophage; Escherichia coli; CRISPR; Feces; Microbiology; Lysogenic cycle; Genetics; Bacteriophage; Gene","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":[0.0003179408,0.0004854413,0.0004351324,0.001010222,0.0004112909,0.0008441749,0.0003365336,0.0003448489,0.002041612],"category_scores_gemma":[0.001360028,0.0001664709,0.0004433746,0.001789213,0.0003140832,0.000474991,0.0004590733,0.0004480093,0.001001053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003399017,"about_ca_system_score_gemma":0.0004447889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002620611,"about_ca_topic_score_gemma":0.005439572,"domain_scores_codex":[0.999584,0.00006162147,0.00002798838,0.0001817916,0.00008148629,0.00006306233],"domain_scores_gemma":[0.9994333,0.0002085606,0.0001705576,0.00005970962,0.00008004459,0.00004763954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001060409,0.0002034279,0.4487942,0.004615542,0.0007506758,0.00121157,0.0006995754,0.04744805,0.2960991,0.004692306,0.01988703,0.1745381],"study_design_scores_gemma":[0.00004347736,0.0003659144,0.6836311,0.0004557585,0.0004615924,0.002155103,0.001018982,0.0960059,0.1101947,0.01471004,0.0908262,0.0001311884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9007032,0.001731643,0.04538216,0.0004617267,0.00005417601,0.00008334178,0.04256816,0.001842373,0.007173227],"genre_scores_gemma":[0.8969036,0.001209616,0.04484516,0.0002423682,0.00001950298,0.0001047653,0.05468228,0.0002485212,0.001744148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002620611,"threshold_uncertainty_score":0.006829858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01205726318715176,"score_gpt":0.2726439083357189,"score_spread":0.2605866451485672,"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."}}