{"id":"W2561038183","doi":"10.1128/microbe.9.204.1","title":"CRISPR-Cas Systems: Making the Cut","year":2014,"lang":"en","type":"article","venue":"Microbe Magazine","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"CRISPR; Computer science; Computational biology; Biology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000202667,0.0001357695,0.0001060469,0.00002264352,0.0000775092,0.00004706933,0.0002128864,0.00007509866,0.00001986407],"category_scores_gemma":[0.00004568821,0.000101551,0.00006283692,0.00005981168,0.00003761424,0.000001261386,0.0001222982,0.00008014374,0.000115183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007857964,"about_ca_system_score_gemma":0.00001059503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008747374,"about_ca_topic_score_gemma":0.00002138461,"domain_scores_codex":[0.9992838,0.00003855185,0.0001533519,0.0002243279,0.00006803037,0.000231928],"domain_scores_gemma":[0.9994572,0.00001132142,0.0000360274,0.0004190496,0.00004180019,0.00003461375],"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":[0.0000102758,0.00001283538,0.0003863966,0.00004015371,0.00002650734,0.000001530641,0.00004343146,0.00133793,0.9425182,0.0001872766,0.05410708,0.001328331],"study_design_scores_gemma":[0.0002594848,0.00006910529,0.002106347,0.00002283593,0.00002057808,0.00007698924,0.00003360143,0.001107945,0.1954377,0.00001122055,0.8006674,0.0001867971],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3098313,0.01205032,0.6521528,0.001361527,0.001630066,0.0005566437,0.00002672153,0.0001027636,0.02228791],"genre_scores_gemma":[0.9955335,0.00005832256,0.0005546046,0.0004028487,0.0005532507,0.00001425794,0.00002624583,0.00002797194,0.002829015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7470806,"threshold_uncertainty_score":0.414113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006822451885739949,"score_gpt":0.2772290932668746,"score_spread":0.2704066413811347,"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."}}