Mechanical blossom thinning of apples and influence on yield, fruit quality and spur leaf area
Bibliographic record
Abstract
McClure, K. A. and Cline, J. A. 2015. Mechanical blossom thinning of apples and influence on yield, fruit quality and spur leaf area. Can. J. Plant Sci. 95: 887–896. Apple (Malus×domestica Borkh.) trees tend to crop heavily, which often makes crop load adjustment necessary. This can now be achieved as early as bloom by mechanical removal/thinning of blossoms. High-density Empire/M.26 and Royal Gala/M.26 apple trees were mechanically (MBT) and hand blossom thinned (HBT) in 2010 and 2011, respectively, and their effects on fruit set, subsequent hand thinning, final crop load, and spur leaf area were measured. In both years, MBT effectively thinned trees and reduced fruit set, but did not reduce the requirement for follow-up hand fruitlet thinning after June drop in 2011. In 2010, harvest yields for MBT treatments decreased, while weight and diameter increased. In 2011, most harvest and fruit quality parameters were unaffected by thinning. Trees that were mechanically thinned had significantly reduced spur leaf area, but were similar to unthinned control trees with respect to many of the yield and quality parameters measured. Mechanical blossom thinning is a new crop load management option for apple growers looking to supplement more traditional chemical and hand thinning techniques.
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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".