{"id":"W2897791282","doi":"10.1089/crispr.2018.0043","title":"A Unified Resource for Tracking Anti-CRISPR Names","year":2018,"lang":"en","type":"letter","venue":"The CRISPR Journal","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Toronto","funders":"National Institute of General Medical Sciences; Novo Nordisk Fonden","keywords":"CRISPR; Resource (disambiguation); Computer science; Biology; Genetics; Gene; Computer network","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":[0.005902672,0.0007198526,0.001130859,0.002260338,0.002846405,0.005722832,0.002689511,0.01230663,0.04544638],"category_scores_gemma":[0.03251944,0.0008465677,0.0007464169,0.001907275,0.002258432,0.007381915,0.005690449,0.01590306,0.0511872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003196092,"about_ca_system_score_gemma":0.004506636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001702958,"about_ca_topic_score_gemma":0.003741686,"domain_scores_codex":[0.9943056,0.001328132,0.0006666572,0.0005175633,0.002664884,0.0005172164],"domain_scores_gemma":[0.9797686,0.006174739,0.001362806,0.003077746,0.006536703,0.003079453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002369913,0.00001274292,0.00006593126,0.00007584141,0.00000544935,0.0002158933,0.00004745501,0.00003195222,0.0009608223,0.01012242,0.9426762,0.04576165],"study_design_scores_gemma":[0.000007881781,0.000008801236,0.00006108645,0.00005141028,0.000002537445,0.0002088321,0.00002140432,0.00006122141,0.0002473987,0.002698009,0.996623,0.000008473191],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"dataset","genre_scores_codex":[0.0008674018,0.0218183,0.0341215,0.7309362,0.133713,0.0002326326,0.002699205,0.004515231,0.07109667],"genre_scores_gemma":[0.01389436,0.02087035,0.04911205,0.6139652,0.06930616,0.0009929857,0.006499905,0.002355804,0.2230033],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.04544638,"threshold_uncertainty_score":0.1520333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01856063147560924,"score_gpt":0.3178383942852925,"score_spread":0.2992777628096832,"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."}}