{"id":"W4401495925","doi":"10.5376/rgg.2024.15.0015","title":"Nutrient Content and Yield in Rice: Genetic Intersections and Breeding Opportunities","year":2024,"lang":"en","type":"article","venue":"Rice Genomics and Genetics","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Agronomy; Nutrient; Biotechnology; Biology; Agricultural engineering; Engineering; Ecology; Materials science","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":[0.00008660783,0.00008936822,0.0000857647,0.0000254125,0.0000939544,0.0001534445,0.00003889357,0.00004702155,0.00001612659],"category_scores_gemma":[0.000008965297,0.00004367676,0.00001611168,0.0000869894,0.00004582058,0.00003631854,0.00007683492,0.00007429753,0.000001001956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001820565,"about_ca_system_score_gemma":0.000006362383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001751007,"about_ca_topic_score_gemma":0.000270942,"domain_scores_codex":[0.9994459,0.00001050812,0.0001592166,0.0002000784,0.00005273869,0.0001316259],"domain_scores_gemma":[0.9997716,0.00008326639,0.00002335143,0.00002364336,0.00002077982,0.00007734976],"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":[0.0000103655,0.00004259158,0.006030631,0.00007295816,0.00002431677,0.00001126812,0.001769601,0.00000440947,0.8021428,0.001121348,0.0003104382,0.1884593],"study_design_scores_gemma":[0.0004482154,0.001018565,0.8592488,0.0001757367,0.00006591244,0.0001046027,0.03105959,0.007018866,0.006244776,0.002016381,0.09195857,0.0006399568],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900634,0.007212763,0.00004183663,0.002091901,0.0001423408,0.0001423288,0.00001702479,0.0000158687,0.0002725345],"genre_scores_gemma":[0.9834601,0.01559642,0.0001329831,0.0004087667,0.00009250906,0.000009156194,0.000004978946,0.000001150037,0.0002939025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8532182,"threshold_uncertainty_score":0.1781087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1033495808178579,"score_gpt":0.2379945842071101,"score_spread":0.1346450033892522,"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."}}