{"id":"W3106971247","doi":"10.1186/s12864-020-07259-6","title":"Metabolomic and transcriptomic analyses of mutant yellow leaves provide insights into pigment synthesis and metabolism in Ginkgo biloba","year":2020,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Plant Gene Expression Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Special Fund for Forest Scientific Research in the Public Welfare; National Key Research and Development Program of China; Priority Academic Program Development of Jiangsu Higher Education Institutions; Government of Jiangsu Province","keywords":"Ginkgo; Ginkgo biloba; Flavonoid biosynthesis; Mutant; Biology; Pigment; Transcriptome; Metabolomics; Carotenoid; Flavonoid; Chlorophyll; Botany; Metabolic pathway; Photosynthesis; Biochemistry; Metabolism; Gene; Gene expression; Chemistry; 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":[0.0001182784,0.0002017858,0.0004372938,0.0001200731,0.00003639026,0.00002171551,0.0001700392,0.0001164022,0.000006785312],"category_scores_gemma":[0.0001104539,0.0001745335,0.0001036154,0.0001385317,0.0001055589,0.000007482933,0.0001186873,0.00006931276,0.000001616656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001216431,"about_ca_system_score_gemma":0.00009410219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001059987,"about_ca_topic_score_gemma":0.0004076889,"domain_scores_codex":[0.9987765,0.0001252435,0.0003741705,0.000460095,0.00009892616,0.0001650401],"domain_scores_gemma":[0.9994563,0.00003141399,0.0001294422,0.000223292,0.00002675169,0.0001328097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001662815,0.00003133443,0.001290598,0.00005467298,0.0001456011,0.000002010919,0.000638242,0.0003576252,0.9964709,0.00001449022,0.0001038069,0.0007244206],"study_design_scores_gemma":[0.000480555,0.00003661406,0.00241142,0.00001166171,0.0002383647,0.000003258481,0.0004206118,0.001798973,0.9865476,0.00005771351,0.007794028,0.0001991966],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990406,0.00797256,0.001157259,0.0001709093,0.00002714047,0.0001756632,0.00003306358,0.000006816722,0.00005055869],"genre_scores_gemma":[0.9910897,0.004264965,0.004319383,0.0001938251,0.00004957102,0.00001638507,0.00002241518,0.00001960193,0.00002418075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009923314,"threshold_uncertainty_score":0.7117268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02523692069935662,"score_gpt":0.2546803896040559,"score_spread":0.2294434689046993,"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."}}