Response of Royal Gala apples to multiple applications of chemical thinners and the dynamics of fruitlet drop
Bibliographic record
Abstract
Apple trees bear an abundance of flowers that produce a surplus of fruit that the tree is unable to support. A self-regulatory mechanism enhances the abscission of immature fruitlets, leading to a reduced fruit load, but this is often insufficient to achieve fruit of marketable size. Supplementary fruit thinning is usually required to optimize economic fruit load and annual bearing. This 2 yr study investigated the response of Royal Gala apple trees to primary single and combination sprays of 6-benzyladenine (6-BA) and carbaryl during fruit set, followed by secondary sprays of either treatment 7 d later. The objective was to determine the effect of a second application of chemical thinner on fruitlet abscission, which compounds are most efficacious, and to characterize the pattern of fruitlet drop. The combinations of chemical thinners varied in their effectiveness at reducing crop density. About 12–14 d were required from the time of the first application of chemical thinner to observed fruitlet beneath trees. A single application of both thinners applied at 8 mm advanced fruit drop by 7 d in most instances. The tank-mix of 6-BA and carbaryl applied at 8–10 mm followed by a secondary spray of carbaryl at 15 mm was the most efficacious thinning combination. Secondary sprays of 6-BA at 15 mm were ineffective at inducing additional fruit drop. Combination sprays of 6-BA and carbaryl were more effective than repeat sprays of the same product. This study enhances our understanding of the dynamics of fruitlet abscission and the benefits of multiple applications of post-bloom chemical thinners.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".