Bibliographic record
Abstract
Camellia sinensis, the tea plant, is an economically valuable crop globally due to its unique flavor, nutritional content, and cultural significance.Tea quality is largely a result of a versatile array of secondary metabolites, such as polyphenols, alkaloids, amino acids, and volatile aroma compounds, which are also largely involved in plant defense and environmental tolerance.New findings in plant molecular biology have allowed the identification in great detail of major biosynthetic pathways like the phenylpropanoid-flavonoid pathway, the MVA/MEP terpenoid biosynthetic pathways, purine and caffeine metabolism, and the theanine biosynthesis.Moreover, studies in mechanisms of regulation-spanning from transcription factors and non-coding RNAs to epigenetic modificationshave unraveled multilayered control mechanisms governing the biosynthesis of metabolites.The integration of transcriptomics, metabolomics, proteomics, and epigenomics has further revealed the spatial-temporal gene expression and metabolic dynamics upon environmental stimuli.The recent advances in tea plant secondary metabolism research are reviewed, application of gene editing, marker-assisted selection, and synthetic biology in metabolic engineering highlighted, and prospects and challenges in the future are elaborated.Increased understanding of secondary metabolic networks and their regulation will provide the major tools for molecular breeding and ensure the introduction of sustainable development in the tea industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".