Study on the Technology of Regulating the Flowering Period of Loquat and Its Influence on the Fruit Ripening Period
Bibliographic record
Abstract
Loquat ( Eriobotrya japonica ) is a subtropical fruit tree with high economic value, especially in southern China, where early market entry can significantly improve profitability. This study reviews the current biological understanding and technological progress on the regulation of loquat flowering time, focusing on its impact on fruit ripening time. We first investigated the endogenous hormonal mechanisms, environmental factors, and genetic factors that control loquat flowering and ripening. Subsequently, we summarized the main technologies used to regulate flowering time, including the application of plant growth regulators, agronomic techniques, and environmental treatments, and evaluated their subsequent effects on fruit quality, harvest time, and market supply. In addition, we discussed molecular biological methods such as gene identification, gene editing, and transcriptomics as emerging strategies for precision flowering regulation. Environmental impacts were also analyzed, and a case study from Guangdong Province was used to illustrate practical applications and farmers' responses. This study believes that effective flowering regulation can not only extend the supply season of loquat and improve market competitiveness, but also lay the foundation for future innovations in precision cultivation and sustainable loquat production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".