{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008773964,0.001164961,0.001117336,0.001174301,0.000839436,0.002461006,0.001446254,0.001574289,0.007191002],"category_scores_gemma":[0.001055649,0.0009751859,0.0007203477,0.0008040128,0.001221363,0.001181235,0.001484748,0.003157443,0.007287708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008066737,"about_ca_system_score_gemma":0.0007526191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008916567,"about_ca_topic_score_gemma":0.0009284047,"domain_scores_codex":[0.9985076,0.000116606,0.000100004,0.0004227862,0.0007275337,0.0001254874],"domain_scores_gemma":[0.9996088,0.00007942487,0.00008395797,0.00006889743,0.00008590726,0.00007293804],"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":[0.0003261368,0.0001392898,0.0008571517,0.0008250493,0.000106886,0.0004894553,0.0002339676,0.001796284,0.7717531,0.02516758,0.03368959,0.1646154],"study_design_scores_gemma":[0.00008416877,0.0004057395,0.0008527666,0.000129917,0.00007137033,0.001482583,0.00007296298,0.003600619,0.5001457,0.007138115,0.4858923,0.0001237407],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.08801229,0.03940342,0.6922725,0.005063295,0.005899486,0.002217163,0.008193442,0.03344672,0.1254916],"genre_scores_gemma":[0.3043522,0.0313712,0.5280364,0.005032674,0.0005821919,0.001328463,0.01604136,0.002276711,0.1109789],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007191002,"threshold_uncertainty_score":0.02405632,"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."}}