{"id":"W2748595563","doi":"10.1139/gen-2017-0100","title":"Rapid and targeted introgression of <i>fgr</i> gene through marker-assisted backcrossing in rice (<i>Oryza sativa</i> L.)","year":2017,"lang":"en","type":"article","venue":"Genome","topic":"GABA and Rice Research","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kementerian Pertanian dan Industri Asas Tani Malaysia","keywords":"Backcrossing; Introgression; Oryza sativa; Biology; Marker-assisted selection; Grain quality; Biotechnology; Background selection; Genetics; Molecular marker; Gene; Genetic marker; Agronomy","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":[0.0003599664,0.0001257147,0.0002308986,0.00001509447,0.0005094758,0.000141719,0.0003442759,0.0001016473,0.0001810437],"category_scores_gemma":[0.0000767828,0.00004783816,0.00005312493,0.0001727924,0.0002471482,0.0002111536,0.0002515762,0.0001481633,0.00001189704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001960668,"about_ca_system_score_gemma":0.00001391092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005281115,"about_ca_topic_score_gemma":0.0001646637,"domain_scores_codex":[0.998803,0.0001057295,0.0002298304,0.0002907949,0.0002208639,0.0003498411],"domain_scores_gemma":[0.9994403,0.0001143566,0.0001692973,0.0001306938,0.00005991582,0.00008545658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005132983,0.00006089328,0.01313368,0.00001974059,0.000007693032,0.00001136125,0.0001394138,2.759954e-7,0.9372371,0.000009941634,0.00002719829,0.04930137],"study_design_scores_gemma":[0.0003064334,0.000120976,0.9839208,0.00003443907,0.000004362824,0.000008707034,0.0001629688,0.00001136062,0.01042898,0.0001659847,0.004711994,0.0001230335],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940039,0.002553634,0.000001620481,0.001166829,0.00006148125,0.0001691619,0.00003007698,0.00001531471,0.001997974],"genre_scores_gemma":[0.9983124,0.0008365755,0.0004473353,0.00006484064,0.0001193055,0.000006669719,0.00002243438,0.000001387908,0.0001890929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.970787,"threshold_uncertainty_score":0.3918528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03811791487626981,"score_gpt":0.277372088833322,"score_spread":0.2392541739570522,"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."}}