{"id":"W1186206053","doi":"10.13271/j.mpb.013.000581","title":"拟南芥、水稻和番茄SWEET/MtN3/saliva基因家族的分析","year":2015,"lang":"zh","type":"article","venue":"分子植物育种","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Saliva; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003685058,0.0004304816,0.000332329,0.00008324116,0.0001368351,0.0001151155,0.0004512931,0.0004604335,0.0001605186],"category_scores_gemma":[0.0002434263,0.0004746397,0.0002778459,0.0001649397,0.0002584266,0.000008686347,0.0002718669,0.0001673771,0.0005454347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005627556,"about_ca_system_score_gemma":0.0005200937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007560996,"about_ca_topic_score_gemma":0.0000520507,"domain_scores_codex":[0.9974554,0.0002129993,0.0004540547,0.0008021011,0.0003950493,0.000680409],"domain_scores_gemma":[0.9974799,0.00001452593,0.0001527842,0.001001311,0.0003995169,0.0009519957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001439384,0.003520545,0.05425846,0.0005777954,0.0007742976,0.0002840447,0.001284304,0.0002164509,0.144379,0.005875152,0.7709017,0.01648882],"study_design_scores_gemma":[0.008042741,0.001832602,0.009628865,0.0001458138,0.0004412524,0.00009149272,0.001271798,0.0001046278,0.06747811,0.01381875,0.8951998,0.001944096],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6644098,0.08304079,0.001610832,0.002838715,0.004568497,0.001023687,0.0007313535,0.0001380567,0.2416383],"genre_scores_gemma":[0.983238,0.001584293,0.0006918544,0.001157022,0.003412317,0.00003324284,0.0006590167,0.00008620624,0.00913798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3188283,"threshold_uncertainty_score":0.9997705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220948738053614,"score_gpt":0.2657633685080244,"score_spread":0.243668494702663,"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."}}