{"id":"W3096307756","doi":"10.1101/2020.10.29.360404","title":"Gene Drives Across Engineered Fitness Valleys: Modeling a Design to Prevent Drive Spillover","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gene drive; Spillover effect; Population; Homing (biology); Computer science; Positive feedback; Control theory (sociology); Engineering; Biology; Gene; Ecology; Artificial intelligence; Economics; CRISPR; Electrical engineering; Genetics; Control (management)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003124448,0.0003345652,0.0003532971,0.0003252783,0.0002837951,0.000865033,0.0007288245,0.001061051,0.002847473],"category_scores_gemma":[0.0007877751,0.0002428461,0.0004971516,0.0001810383,0.0005890185,0.0004357396,0.0004929594,0.0004790638,0.0002642164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008324193,"about_ca_system_score_gemma":0.0007402527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003872105,"about_ca_topic_score_gemma":0.002439029,"domain_scores_codex":[0.9999148,0.00002180315,0.00000314537,0.00001990209,0.00001920065,0.00002127549],"domain_scores_gemma":[0.999751,0.0001036083,0.00006108114,0.00001820211,0.00003390282,0.00003212718],"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":[0.00002730306,0.00004247445,0.0005209399,0.00003373858,0.000009393217,0.00005966528,0.00002893188,0.9814112,0.005764781,0.009584472,0.0001764698,0.002340637],"study_design_scores_gemma":[0.00001271868,0.00004761258,0.0001210688,0.000003836258,0.000006622482,0.00001341936,0.000009242512,0.9970831,0.0005266755,0.00177902,0.0003927543,0.000003908938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6054629,0.0005248636,0.3608916,0.0008355709,0.00005708538,0.0001512278,0.0002746658,0.0003691681,0.03143295],"genre_scores_gemma":[0.9804997,0.0001793361,0.0134914,0.00004662227,0.000005631746,0.00010696,0.00003648517,0.0000280017,0.005605913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003872105,"threshold_uncertainty_score":0.009525716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01636245730153704,"score_gpt":0.2644565412863944,"score_spread":0.2480940839848574,"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."}}