{"id":"W2371073355","doi":"","title":"Research Progress and Prediction in Rice Proteomics","year":2011,"lang":"en","type":"article","venue":"Seed","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Proteomics; Biology; Computational biology; Genetics","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":[0.000448436,0.00004847292,0.00004401378,0.00005407694,0.00003788691,0.00001099708,0.00007802418,0.00008791607,0.000004172841],"category_scores_gemma":[0.00007469569,0.00004467601,0.00000865399,0.00008633083,0.00008683588,0.000003018096,0.0000924689,0.0001497492,0.000008904456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007143328,"about_ca_system_score_gemma":0.00002364112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003194211,"about_ca_topic_score_gemma":0.0000150731,"domain_scores_codex":[0.9994984,0.00005716873,0.0001025152,0.0001114169,0.00008428226,0.0001461911],"domain_scores_gemma":[0.9997519,0.00000409139,0.00002505194,0.0001403837,0.00004655813,0.00003197109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002599879,0.0001182829,0.8904775,0.0001490541,0.00002212249,0.000004062395,0.002119261,0.00001118249,0.1037255,0.0004013854,0.0004050934,0.002306516],"study_design_scores_gemma":[0.0005021273,0.00041858,0.9552112,0.00002016602,0.000002686458,0.00001950602,0.000261169,0.002039029,0.03952594,0.0001975241,0.001704304,0.00009775089],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907406,0.0001000772,0.0001883243,0.00004209298,0.00003260706,0.000243277,0.000002749493,0.000009643592,0.008640622],"genre_scores_gemma":[0.9918185,0.00001887979,0.007599838,0.00002303813,0.00004730988,0.00003042092,0.00002041432,0.000007707901,0.0004338169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06473366,"threshold_uncertainty_score":0.1821835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02476846992869483,"score_gpt":0.3009495143972294,"score_spread":0.2761810444685345,"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."}}