{"id":"W7106824095","doi":"10.5281/zenodo.17721959","title":"CRISPR Accuracy Engineering via Framework 50 Computational Redundancy: Achieving High-Confidence Editing Fidelity Through Convergent Validation","year":2025,"lang":"","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Society for the Study of Architecture in Canada","funders":"","keywords":"CRISPR; Redundancy (engineering); Computational model; Computational complexity theory; Fidelity; Source code; In silico; Key (lock)","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":[0.003637476,0.0007785336,0.0006874619,0.0006421299,0.0005340526,0.001476056,0.001583815,0.000807696,0.002518662],"category_scores_gemma":[0.008014572,0.0004498758,0.001156672,0.0002315482,0.00136336,0.0009574354,0.001799235,0.001235939,0.0006641482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001325964,"about_ca_system_score_gemma":0.003060589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003224438,"about_ca_topic_score_gemma":0.002032627,"domain_scores_codex":[0.9971498,0.0006918739,0.0001246959,0.000404671,0.001333201,0.0002957605],"domain_scores_gemma":[0.9976372,0.001022734,0.0002452087,0.0006155299,0.000391061,0.00008834797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002869618,0.0001685227,0.004602893,0.000262148,0.0001347887,0.0003800599,0.0003281148,0.6677073,0.06285146,0.1824921,0.002066228,0.07871944],"study_design_scores_gemma":[0.00005904402,0.0003685429,0.0006538162,0.00008493958,0.00006275116,0.000187443,0.00004623399,0.9050021,0.04617145,0.03818029,0.009116003,0.00006743887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06531244,0.0001733929,0.9215698,0.0001974563,0.00004520291,0.0001758951,0.0001560004,0.002779771,0.009589953],"genre_scores_gemma":[0.643698,0.0001571879,0.3527401,0.0001782474,0.00001978384,0.0003406919,0.0004122266,0.0004685341,0.001985196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003637476,"threshold_uncertainty_score":0.01923704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198054363562525,"score_gpt":0.3021967540380984,"score_spread":0.2823913176818459,"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."}}