Impact of Chemical and Non-Chemical Thinning Treatments on Yield and Fruit Quality of Date Palm
Bibliographic record
Abstract
The fruit thinning process is employed for the production of high quality large-sized fruits and prevent the production of compact bunches. It is also one way to reduce the alternate bearing habits in date palm. In this study, seven thinning treatments (i.e. without spraying water after pollination, spraying water at 3 minutes after 3, 4 and 5 h, spraying Ethephon at 0, 500 and 1000 ppm after ten days from pollination) are used for Khalas and Ruzeiz date palm cultivars. The factorial experiment in a randomized completely block design with three replicates was done. The results reveal that, spraying water after mechanical pollination has reduced fruit set% and increased fruit shees%. Most thinning treatments reduced fruit yield/palm in both Khalas and Ruzeiz. Spraying water after 5 h enhanced fruit quality compared with the other thinning treatments in besr and tamr stages. Spraying with ethephon at 1000 ppm gave the increased sugars content and TSS, whereas reduced the moisture content in besr stage. Spraying water after 5 h from mechanical pollination or Ethephon at 1000 ppm after 10 days are suitable for obtaining economic yield with best fruit quality.
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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".