Influence of prohexadione calcium (Apogee<sup>®</sup>) on shoot growth of non-bearing mature apple trees in two different growing regions
Bibliographic record
Abstract
Orchard experiments were conducted on mature non-bearing apple ( Malus × domesticaBorkh.) trees to determine the efficacy of prohexadione-calcium (PC), formulated as Apogee® [27.5% PC + 56.1% (NH4)2 SO4 +16.4% ot her proprietary additives] for shoot growth control on six cultivars grown in Ontario (ON) and one grown in New Brunswick (NB), Canada. Seasonal patterns of extension shoot growth among cultivars in both locations were also compared. Results indicate that PC applications are most effective at the beginning of the season, when relative growth rates were greatest in eastern Canada. Four applications of PC failed to significantly reduce shoot growth more than two applications in either location. However, the level of control might have been more effective in ON if treatment applications were initiated earlier in the season. Although tree vigor and shoot growth differed between cultivars and locations, PC significantly and consistently reduced shoot growth and relative shoot growth rates for all cultivars at both locations. Empire shoots treated with PC were approximately 33 and 37% shorter at the end of the season in NB and ON, respectively. Key words: Calcium 3-oxido-5-oxo-4-propionylcyclohex-3-enecarboxylate, anti-giberellin, plant growth regulator, relative growth rate, Malus
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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".