The levels of hormone and carbohydrate in autumn and non-autumn flowering tree peonies
Bibliographic record
Abstract
Mornya, P. M. P. and Cheng, F. 2011. The levels of hormone and carbohydrate in autumn and non-autumn flowering tree peonies. Can. J. Plant Sci. 91: 991–998. This study analyzed the levels of gibberellic acid (GA3), indole-3-acetic acid (IAA), cytokinin (CTK) and abscisic acid (ABA) hormones using high-performance liquid chromatography, along with the levels of sucrose, reducing sugar and starch carbohydrates by spectrophotometer during induction, initiation and differentiation stages of bud development in autumn (AFP) and non-autumn (NAFP) flowering tree peony cultivars exhibiting variations in flowering pattern. The experimental design was a randomized complete block with three replications. The variation in flowering pattern between AFP and NAFP was largely influenced by differences in GA3, IAA and CTK levels at different bud developmental stages. The flower formation cycle was completed earlier in AFP than in NAFP, hence flowering was twice annually. Cytokinin, particularly N6-(Δ2-isopentenyl)-adenosine (iPA), could be a critical hormone in autumn flowering of tree peony, as its differences in levels between AFP and NAFP remained significant across bud developmental stages. However, only GA3had negative differences across bud developmental stages, indicating that GA3levels were consistently higher in NAFP than in AFP, but the reverse holds true for CTK. The differences in GA3, IAA and CTK between AFP and NAFP were significant (P<0.05) for at least two-thirds of the bud developmental stages. Carbohydrates may not significantly influence the flowering pattern of tree peonies. Autumn flowering in tree peonies could therefore be achieved by regulating GA3, IAA and CTK levels, particularly at the induction and initiation stages of bud development to facilitate the completion of the floral formation cycle, well in advance of bud dormancy period. The findings of this study could lay the scientific basis for genetic engineering of flowering pattern of tree peonies.
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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".