{"id":"W3134954922","doi":"10.1093/nar/gkab133","title":"Streamlining CRISPR spacer-based bacterial host predictions to decipher the viral dark matter","year":2021,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Danish Agency for Science and Higher Education; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Canada First Research Excellence Fund; Canada Research Chairs; Novo Nordisk Fonden; Canadian Allergy, Asthma and Immunology Foundation","keywords":"Biology; CRISPR; Host (biology); Computational biology; Human virome; Metagenomics; Bacteriophage; Genome; Genetics; Gene; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004360468,0.0001390358,0.0001268827,0.00005839049,0.0007368386,0.0003375152,0.000374214,0.0000926374,0.05756647],"category_scores_gemma":[0.0001407004,0.0001086917,0.00009480227,0.0005360844,0.0002770256,0.0002350554,0.0006084656,0.0005544547,0.008697514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002784033,"about_ca_system_score_gemma":0.00007114055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006788103,"about_ca_topic_score_gemma":0.0007313087,"domain_scores_codex":[0.9980167,0.0002805968,0.0002098587,0.00044647,0.0004614234,0.0005849696],"domain_scores_gemma":[0.998911,0.0001775811,0.00002911974,0.0006086208,0.00006694052,0.0002067158],"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":[0.00007453653,0.0001468847,0.005875436,0.000004116331,0.00001777901,0.00002496142,0.0005196267,0.00014179,0.7923364,0.00001387433,0.1986431,0.002201505],"study_design_scores_gemma":[0.0005231675,0.0002034408,0.446133,0.00005121387,0.00001994022,0.00003528485,0.001144487,0.0002943277,0.03796437,0.00005307662,0.5133193,0.0002583741],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9403059,0.000008514985,0.0007296096,0.01054403,0.0005511553,0.0002994626,0.0000836145,0.00004162637,0.04743601],"genre_scores_gemma":[0.9865143,0.000005963757,0.001630498,0.001097154,0.0002850157,0.00006297712,0.00002586925,0.00003628399,0.01034194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7543721,"threshold_uncertainty_score":0.9920743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03063974357446398,"score_gpt":0.3305132384889827,"score_spread":0.2998734949145188,"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."}}