{"id":"W4225421495","doi":"10.1096/fasebj.2022.36.s1.0i111","title":"CRISPR sensors for signaling","year":2022,"lang":"en","type":"article","venue":"The FASEB Journal","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Muscular Dystrophy Canada","funders":"","keywords":"CRISPR; Computational biology; Computer science; Biology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":{"nature":"Retraction","reason":"Error by Journal/Publisher;","date":"5/27/2022 0:00","openalex_flagged":false},"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005052227,0.001125521,0.0006758818,0.0009362623,0.0007726355,0.001761586,0.0009626407,0.001498798,0.00914462],"category_scores_gemma":[0.0007698698,0.0006099621,0.0006253777,0.0003750446,0.0009159602,0.0008224538,0.001383471,0.003643363,0.008630108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278823,"about_ca_system_score_gemma":0.0006425311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003793223,"about_ca_topic_score_gemma":0.001075136,"domain_scores_codex":[0.9989692,0.00008285283,0.0000748196,0.0002458079,0.0004610731,0.0001662572],"domain_scores_gemma":[0.9993593,0.0001222977,0.0001350528,0.0001699531,0.00007708336,0.0001362787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001450856,0.00004482372,0.0002213834,0.0001374825,0.00002471893,0.0001427425,0.00005056584,0.0002175854,0.9552661,0.01134048,0.007959463,0.0244495],"study_design_scores_gemma":[0.00002124408,0.00005551999,0.000735559,0.0000224014,0.00001635651,0.0005141108,0.00002659111,0.00112657,0.9179332,0.002178991,0.07734023,0.00002923436],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2837919,0.01231447,0.4434769,0.009161214,0.006553953,0.0005980787,0.01442916,0.05455316,0.1751213],"genre_scores_gemma":[0.7836972,0.004219198,0.1002916,0.003908414,0.0004155205,0.0004656619,0.009008361,0.002767627,0.09522633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00914462,"threshold_uncertainty_score":0.03059185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115735099598137,"score_gpt":0.2869184940291691,"score_spread":0.2753449840693554,"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."}}