{"id":"W4405851924","doi":"10.5376/gab.2024.15.0020","title":"Genome Editing and Rice Improvement: The Role of CRISPR/Cas9 in Developing Superior Yield Traits","year":2024,"lang":"en","type":"article","venue":"Genomics and Applied Biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"CRISPR; Genome editing; Yield (engineering); Biology; Genetics; Biotechnology; Computational biology; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001521035,0.0001075188,0.0001210053,0.00003858879,0.00004003308,0.00001901424,0.0000790398,0.0001026528,0.000002201631],"category_scores_gemma":[0.000007872158,0.00008457548,0.00002092514,0.00005356558,0.00006466213,0.00000120262,0.0001271013,0.00007855807,5.332086e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007055492,"about_ca_system_score_gemma":0.00002737615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001600972,"about_ca_topic_score_gemma":0.00002750468,"domain_scores_codex":[0.9993663,0.000006234626,0.0001728748,0.0002517411,0.0000201776,0.0001826563],"domain_scores_gemma":[0.9998412,0.00002224632,0.00002099676,0.00008028334,0.000008958839,0.00002629363],"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":[0.000008359491,0.000003168078,0.0001458026,0.00003929863,0.00002189179,3.888432e-7,0.0002048487,0.00002897698,0.9786855,0.00264623,0.000002926656,0.01821259],"study_design_scores_gemma":[0.0002721436,0.000147095,0.01075,0.00001878483,0.00002110859,0.00002506281,0.001006893,0.0002895948,0.9262471,0.001302232,0.05966183,0.0002582129],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865879,0.009741649,0.002872433,0.0001533695,0.00006723029,0.0001471007,0.0000207202,0.000005718795,0.0004038262],"genre_scores_gemma":[0.9977083,0.001320459,0.0006087783,0.000105594,0.0001920108,0.00002081181,0.00001908301,0.00001265493,0.00001226953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0596589,"threshold_uncertainty_score":0.3448888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005594737904058873,"score_gpt":0.2494351563650813,"score_spread":0.2438404184610224,"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."}}