{"id":"W6977287907","doi":"10.6084/m9.figshare.28975067","title":"Additional file 4 of Predicting adenine base editing efficiencies in different cellular contexts by deep learning","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acuitas Therapeutics (Canada)","funders":"","keywords":"Deep learning; Base (topology); Position (finance); Focus (optics); File format","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001075558,0.002735429,0.002027737,0.002149551,0.000910688,0.002371376,0.003204061,0.00255843,0.4708397],"category_scores_gemma":[0.007598326,0.0007927472,0.001647099,0.00292228,0.0004293341,0.001557777,0.001413048,0.001987473,0.1939805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0011273,"about_ca_system_score_gemma":0.001669421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006551202,"about_ca_topic_score_gemma":0.01527666,"domain_scores_codex":[0.9993793,0.00008198409,0.0000563992,0.0002620695,0.0001224675,0.00009780176],"domain_scores_gemma":[0.9960084,0.002602526,0.000209079,0.0005285398,0.0004586692,0.0001927252],"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":[0.0001932884,0.00006834071,0.001782705,0.001612061,0.0000815704,0.00004689562,0.00002205692,0.001066421,0.0003384129,0.0004737994,0.9894044,0.004909977],"study_design_scores_gemma":[0.002256489,0.0002039337,0.01042988,0.001077156,0.0002463276,0.0003506232,0.0001158182,0.004600734,0.00297899,0.01185191,0.9657468,0.0001413586],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001146548,0.00003498269,0.0001369754,0.00003202688,0.0000174276,0.00001085421,0.9989793,0.000430261,0.0002435588],"genre_scores_gemma":[0.0009640025,0.00004694735,0.0008222303,0.00009273192,0.00001433782,0.0001537139,0.9966557,0.0002513571,0.0009989132],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4708397,"threshold_uncertainty_score":0.754783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01385428163868427,"score_gpt":0.231818353305098,"score_spread":0.2179640716664137,"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."}}