{"id":"W3042186428","doi":"10.1021/acs.jafc.0c02053","title":"Metabolomic Analyses Provide New Insights into Signaling Mechanisms for Nutrient Uptake by Lateral Roots of Pruned Tea Plant (<i>Camellia sinensis</i>)","year":2020,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Tea Polyphenols and Effects","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Science Foundation of Jiangsu Province; China Postdoctoral Science Foundation; Ministry of Education of the People's Republic of China","keywords":"Phenylpropanoid; Metabolic pathway; KEGG; Metabolism; Camellia sinensis; Biosynthesis; Biology; Biochemistry; Metabolomics; Flavonoid biosynthesis; Nutrient; Citric acid cycle; Amino acid; Metabolome; Botany; Transcriptome; Gene; Gene expression","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009727547,0.000421942,0.0004090959,0.0004892094,0.0002503583,0.0003190457,0.0001263941,0.0002906434,0.001229397],"category_scores_gemma":[0.00008061955,0.0001274358,0.0005091092,0.0003979467,0.0001077021,0.0003837525,0.0002125253,0.0003918493,0.0002113144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002120272,"about_ca_system_score_gemma":0.0001601425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009421972,"about_ca_topic_score_gemma":0.002063402,"domain_scores_codex":[0.9999382,0.000004526188,0.000004184958,0.00002842164,0.00001347013,0.00001118641],"domain_scores_gemma":[0.9999444,0.000009304515,0.00001598454,0.000005997039,0.0000150286,0.000009291975],"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":[0.00007121582,0.00000744606,0.001182777,0.00006720655,0.00001702081,0.00004142573,0.00001363842,0.00002853773,0.996713,0.00003930264,0.00002582131,0.001792621],"study_design_scores_gemma":[0.00002816623,0.0004036888,0.3905204,0.0000402597,0.0002895212,0.0007646681,0.0002810663,0.00404805,0.5926419,0.001002448,0.009929081,0.00005065654],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774925,0.005435018,0.00876904,0.0002328481,0.00003840783,0.0000416698,0.006131464,0.0001823478,0.001676529],"genre_scores_gemma":[0.9817789,0.001954739,0.008201433,0.0002563394,0.00001769548,0.00004530976,0.005635332,0.00006113385,0.002049246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001229397,"threshold_uncertainty_score":0.00411278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950131157648085,"score_gpt":0.23853061091132,"score_spread":0.2190292993348392,"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."}}