Response of Bosc and Cold Snap™ pears to thinning with NAA, 6-BA, ACC, and s-ABA
Bibliographic record
Abstract
Adjusting the crop load of European pears (Pyrus communis L.) by hand thinning is currently required to ensure marketable size of most cultivars grown in Ontario. The benefits of thinning pears chemically and its effect on fruit quality and fresh-market returns were investigated in a 3-yr study where a series of foliar chemical thinning sprays were applied during the 10-mm fruitlet stage to Bosc and Cold Snap™ pear trees growing in commercial orchards in the Niagara Peninsula, Ontario. Treatments included an untreated and hand-thinned control and two concentrations each of 6-benzyladenine (6-BA; 75 and 150 mg L−1), naphthalene acetic acid (10 and 20 mg L−1), 1-aminocyclopropane carboxylic acid (150 and 300 mg L−1), and s-abscisic acid (s-ABA; 150 and 300 mg L−1). Overall, all thinning products reduced crop load at least once in the 3-yr study, although this varied by year and cultivar. Higher concentrations were more effective than lower concentrations. Naphthalene acetic acid (both rates), 150 mg L−1 of 6-BA, and 300 mg L−1 of s-ABA were the most consistent at thinning the crop. As the crop load of control trees was not heavy, minimal hand thinning was required and all thinning treatments reduced crop value compared with the untreated trees. There were minimal effects on starch hydrolysis, soluble solids, fruit firmness, and skin colour at harvest.
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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 | 0.001 |
| 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".