{"id":"W4403548299","doi":"10.1016/j.lfs.2024.123120","title":"CRISPR innovations in tissue engineering and gene editing","year":2024,"lang":"en","type":"review","venue":"Life Sciences","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"CRISPR; Genome editing; Computational biology; Gene; Biology; Computer science; Genetics","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":[0.0009281457,0.001126035,0.001185067,0.002732494,0.0003900747,0.001260343,0.001298547,0.001394539,0.004099228],"category_scores_gemma":[0.0005785685,0.0005386447,0.0008682834,0.002127171,0.0011369,0.001178857,0.001034773,0.002980042,0.003134743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001084477,"about_ca_system_score_gemma":0.001330566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00094762,"about_ca_topic_score_gemma":0.00108591,"domain_scores_codex":[0.9993766,0.00009876881,0.00006784675,0.000118178,0.000269021,0.00006974004],"domain_scores_gemma":[0.9996948,0.000151329,0.00004031862,0.00002287247,0.00005838323,0.0000323272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004852354,0.00007331854,0.0001594132,0.01195803,0.0001016631,0.0004162221,0.0001129482,0.0008742926,0.01802397,0.02860806,0.02730724,0.9123163],"study_design_scores_gemma":[0.00000841815,0.00004785951,0.0002454541,0.0008750467,0.00003461898,0.001048758,0.00002089498,0.0001284086,0.003958836,0.00325806,0.9903511,0.00002251585],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00043094,0.9818183,0.006438632,0.0006528942,0.000731566,0.00003691338,0.00007745155,0.000155378,0.009658003],"genre_scores_gemma":[0.003797112,0.9851496,0.004767749,0.0006605436,0.0003003815,0.00005284225,0.0001557872,0.00002338248,0.00509257],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004099228,"threshold_uncertainty_score":0.01371324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0260392342600306,"score_gpt":0.3789683852541938,"score_spread":0.3529291509941632,"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."}}