{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002598258,0.0001773076,0.0002819177,0.0002038329,0.00004187626,0.00005245941,0.0001687381,0.0001310626,0.000004114719],"category_scores_gemma":[0.0001517184,0.0001487513,0.00004379643,0.0005197285,0.00005405687,0.000003043745,0.0001414729,0.0001364079,0.000008897055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007062591,"about_ca_system_score_gemma":0.0001126083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007713717,"about_ca_topic_score_gemma":0.000008030084,"domain_scores_codex":[0.9990695,0.00001091164,0.0002570661,0.0003684878,0.00009311846,0.0002009792],"domain_scores_gemma":[0.9997671,0.00001421144,0.00003685454,0.0001183882,0.00001284724,0.00005060541],"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":[7.951345e-7,0.00002701016,0.00005588383,0.01438001,0.0001113611,0.00002846879,0.0001456309,0.001606755,0.007281688,0.0007476296,0.005758251,0.9698565],"study_design_scores_gemma":[0.00002405318,0.00003069278,0.00000568631,0.001039925,0.00004262627,0.0000255728,0.00001867602,0.0002085393,0.0005498395,0.000007318017,0.9978633,0.000183752],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002226386,0.9978957,0.001001096,0.0000332079,0.0004802972,0.0001308377,0.00001303601,0.00001480661,0.0002083452],"genre_scores_gemma":[0.0005451581,0.996887,0.001692821,0.00002130222,0.0006402169,0.00003273984,0.00002842018,0.00001877606,0.0001335312],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9921051,"threshold_uncertainty_score":0.6065903,"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."}}