Gibberellic Acid Increases Fruit Firmness, FruitSize, and Delays Maturity of ‘Sweetheart’ SweetCherry
Bibliographic record
Abstract
Growers in British Columbia, Canada and the US Pacific Northwest use gibberellic acid (GA 3) to improve fruit quality of sweet cherries ( Prunus aviumL.). A single spray application of about three weeks before harvest has become the standard procedure. The objective of this trial was to determine if multiple applications of GA 3can further increase fruit firmness and size, and delay maturity of ‘Sweetheart’ sweet cherry, the second most important sweet cherry cultivar in British Columbia. Yield was not affected by a single application of 20 or 30 ppm or two or three weekly applications of 10 ppm GA 3in any of the three years of the trial. Fruit treated with GA 3were significantly firmer than fruit not treated; however, there were no differences in fruit firmness amongst the single or multiple GA 3treatments. Titratable acidity of GA 3-treated fruit was significantly higher than that of untreated fruit. There were no differences in titratable acidity within the GA 3treated fruit. Fruit treated with GA 3were significantly larger than untreated fruit and the fruit treated with 20 ppm GA 3were larger than the fruit treated with 30 ppm GA 3(single applications). In summary, GA 3-treated fruit could be harvested later and were larger and firmer than untreated fruit. There was no benefit to multiple applications of GA 3relative to a single application.
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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".