Bibliographic record
Abstract
The tea tree ( Camellia sinensis ) is a crop of significant economic and cultural value worldwide. However, its yield and quality are often affected by various biological stresses, including fungal diseases and pest attacks. Against the backdrop of intensified ecological pressure and restricted pesticide use, enhancing tea trees' resistance to pests and diseases has become a key task for achieving sustainable development of the tea industry. This study systematically explored the genetic tools used to enhance the resistance of tea plants to biological stress, integrating the latest achievements in traditional breeding, molecular biology and modern biotechnology. It reviewed the genetic and physiological basis of tea plant resistance traits, focusing on key resistance genes, their expression patterns and regulatory mechanisms. Based on the comparison of traditional breeding methods with modern molecular strategies (such as molecular marker-assisted selection MAS, RNA interference RNAi, CRISPR/Cas9 gene editing technology), the advantages of precision breeding in the directional enhanced resistance pathway were demonstrated. The functions of defense signaling networks (especially jasmonic acid JA, salicylic acid SA and ethylene ET pathways) as well as transcription factors and epigenetic regulatory factors in resistance expression were also explored. This study provides a comprehensive overview and practical guidance on the genetic techniques required to enhance the biological stress resistance of tea plants, aiming to promote the in-depth integration of tea plant resistance breeding research and industrial application.
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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".