{"id":"W2254704131","doi":"10.1007/978-3-319-22551-7_8","title":"Genetic Engineering and Precision Editing of Triticale Genomes","year":2015,"lang":"en","type":"book-chapter","venue":"","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Triticale; Biology; Genome; Computational biology; Computer science; Genetics; Gene","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":[0.0003166633,0.0006708046,0.0007401441,0.0006470951,0.0003764849,0.001419822,0.000997666,0.0007495953,0.006710303],"category_scores_gemma":[0.0002907462,0.0004869029,0.0006516352,0.001016286,0.0007186123,0.001264453,0.0009442721,0.002889157,0.004170072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006737576,"about_ca_system_score_gemma":0.0003675822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003435223,"about_ca_topic_score_gemma":0.0006950324,"domain_scores_codex":[0.9998185,0.00001574331,0.000007924967,0.00003975804,0.00009338976,0.00002474851],"domain_scores_gemma":[0.9999259,0.00002905441,0.000008879602,0.00001771382,0.000009572997,0.000008972833],"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":[0.00006090025,0.00006476877,0.00006003303,0.0009203099,0.00002577432,0.0002327729,0.0002469869,0.002022126,0.4518849,0.09066445,0.01614862,0.4376685],"study_design_scores_gemma":[0.00001257047,0.00007827776,0.0003853881,0.0001705594,0.00001967248,0.001141516,0.00004670845,0.001462466,0.1773403,0.03609243,0.7832093,0.00004078174],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03416742,0.2144436,0.5292405,0.003214034,0.003778461,0.0001824657,0.001222219,0.003231724,0.2105195],"genre_scores_gemma":[0.1274596,0.1938558,0.2525062,0.001345446,0.0005793166,0.0003242405,0.003579791,0.001425494,0.4189241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006710303,"threshold_uncertainty_score":0.02244818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01051957722611683,"score_gpt":0.2584034820241972,"score_spread":0.2478839047980804,"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."}}