Bibliographic record
Abstract
This study reviews various yield-enhancing cultivation techniques for cherry tomatoes, including regulated deficit irrigation (RDI), pruning, biostimulants, organic fertilizers, and modern greenhouse technologies. These techniques have shown significant effects in optimizing water and fertilizer management, as well as improving crop yield and quality. For example, a multi-level fuzzy comprehensive evaluation indicates that precise irrigation and nutrient management can significantly enhance tomato growth and yield. Additionally, using drip irrigation systems combined with bio-organic fertilizers, intercropping with leguminous green manures, and utilizing shading nets have proven effective in improving water use efficiency and fruit quality. Optimizing greenhouse environments also significantly boosts yield and fruit quality. Despite the notable yield benefits of these techniques, their promotion faces challenges such as insufficient public awareness, management complexity, and high implementation costs. This study suggests further research directions, including exploring the synergistic effects of combining optimal irrigation with organic fertilizers, developing resilient varieties capable of withstanding various environmental stressors, and utilizing sensors and automation in precision agriculture to enhance efficiency and reduce labor costs.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.036 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".