{"id":"W3107605394","doi":"10.3389/fgene.2020.589326","title":"Temporospatial Flavonoids Metabolism Variation in Ginkgo biloba Leaves","year":2020,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Ginkgo biloba and Cashew Applications","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Doctorate Fellowship Foundation of Nanjing Forestry University; National Natural Science Foundation of China; Nanjing Forestry University","keywords":"Ginkgo biloba; Ginkgo; Flavonoid biosynthesis; Flavonoid; Biology; Transcriptome; Metabolome; Botany; Gene; Biosynthesis; Metabolomics; Secondary metabolism; Gene expression; Biochemistry; Bioinformatics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001142936,0.0001432974,0.000307082,0.0001378155,0.00002819328,0.00001710297,0.0001308994,0.0001428739,0.00003648815],"category_scores_gemma":[0.00006411185,0.0001465835,0.00005568006,0.0005491248,0.00005229975,0.00004135596,0.00004068899,0.00022912,0.00002738073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007407821,"about_ca_system_score_gemma":0.0001206214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004360159,"about_ca_topic_score_gemma":0.00002159029,"domain_scores_codex":[0.9988427,0.00004081567,0.0003502205,0.0003159769,0.0002124579,0.0002378957],"domain_scores_gemma":[0.9994773,0.00001089614,0.0000635071,0.0002572161,0.00004379064,0.0001473391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002851547,0.000434868,0.8796849,0.0001475607,0.00008710629,0.00005074226,0.006317789,0.001355577,0.0358541,0.001377847,0.0433252,0.03107915],"study_design_scores_gemma":[0.002667558,0.0001616952,0.9168038,0.00002699495,0.0001019967,0.000004770187,0.0005102215,0.01684383,0.004752775,0.002001704,0.05584259,0.0002820549],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9106272,0.003461664,0.07135401,0.009874558,0.0008015591,0.00100548,0.00002600637,0.00009278824,0.002756679],"genre_scores_gemma":[0.9399172,0.0003703337,0.05770664,0.001417806,0.0003388389,0.00005616173,0.00003393576,0.00002566302,0.0001333964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0371189,"threshold_uncertainty_score":0.59775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351863868869314,"score_gpt":0.2390037936032335,"score_spread":0.2254851549145404,"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."}}