Recent Advances in the Regulation of Citric Acid Metabolism in Citrus Fruit
Bibliographic record
Abstract
The regulation of citric acid metabolism during fruit ripening has a major impact on the production of high-quality fruit. The impact of citric acid on organoleptic fruit quality attributes, fruit storage performance and the synthesis of several secondary metabolites has led to an exponential increase in research efforts during the last two decades. Recent research has focused on the relationship among citric acid biosynthesis, transportation, storage, and utilization. Among citrate metabolic processes, activities of a proton pump, especially the plasma membrane H+-ATPase on tonoplast and citrate catabolism in cytosol play important roles in the regulation of citrate accumulation in cell vacuoles. Moreover, we highlight recent advances and provide an overview of citrate metabolism, postharvest physiology of citrate metabolism, and the influence of agro-climatic factors on citrus fruits. It is the first review that provides a comprehensive model for citrate metabolism in citrus fruit juice sacs. We anticipate that this model for the regulation of citrate metabolism will facilitate the study of fruit acidity in citrus and other nonclimacteric fruits.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".