{"id":"W4249221593","doi":"10.21203/rs.3.rs-765771/v1","title":"Development of a gRNA-tRNA Array of CRISPR/Cas9 in Combination with Grafting Technique to Improve Gene Editing Efficiency of Sweet Orange","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities; Chinese Academy of Sciences; Institute of Genetics; National Natural Science Foundation of China","keywords":"CRISPR; Guide RNA; Genome editing; Orange (colour); Transfer RNA; Computational biology; Biology; Computer science; Gene; Genetics; Horticulture; RNA","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004328134,0.0005377526,0.0006430434,0.000482246,0.0002849051,0.0006875101,0.0004725577,0.0006215267,0.001879236],"category_scores_gemma":[0.0003159645,0.0002724033,0.0005685591,0.0003184224,0.0001981899,0.000434725,0.0005968533,0.000881962,0.0008988004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002176262,"about_ca_system_score_gemma":0.0001987178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004030028,"about_ca_topic_score_gemma":0.0005723914,"domain_scores_codex":[0.9995769,0.00003748063,0.00004273887,0.000145208,0.0001508477,0.00004680947],"domain_scores_gemma":[0.9998201,0.00003201549,0.00004915725,0.000029671,0.000028094,0.00004104411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001195642,0.00001355588,0.00005488256,0.00002545645,0.000004112292,0.00002938619,0.000007250881,0.00008447364,0.9979492,0.00005418422,0.00003636404,0.001729069],"study_design_scores_gemma":[0.000007989297,0.0000832415,0.000544209,0.000004541338,0.00001818813,0.0001280553,0.000008943546,0.00151126,0.9949944,0.00003793599,0.00264821,0.00001313848],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8381909,0.00223028,0.1491076,0.0003807228,0.0003848902,0.0003851956,0.001100985,0.002694181,0.005525423],"genre_scores_gemma":[0.862852,0.001366903,0.1237284,0.0002074878,0.00003635738,0.0001895135,0.001559402,0.000520202,0.009539862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001879236,"threshold_uncertainty_score":0.006286681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02015671883676785,"score_gpt":0.366148486894191,"score_spread":0.3459917680574232,"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."}}