Harvest date, post-harvest vernalization and regrowth temperature affect flower bud induction in Russian dandelion (<i>Taraxacum kok-saghyz</i>)
Bibliographic record
Abstract
Hodgson-Kratky, K. M. J., Demers, M. N. K., Stoffyn, O. M. and Wolyn, D. J. 2015. Harvest date, post-harvest vernalization and regrowth temperature affect flower bud induction in Russian dandelion (Taraxacum kok-saghyz). Can. J. Plant Sci. 95: 1221–1228. Russian dandelion (Taraxacum kok-sagyz Rodin; TKS) is a promising candidate for introducing natural rubber production into North America; however, a comprehensive analysis of factors that influence flowering is essential for efficient breeding and crop development. The objectives of this study were to determine the effects of fall harvest date (early September, October and November), post-harvest vernalization (0, 4 and 8 wk at 4°C), and greenhouse regrowth temperature [15/13°C or 21/18°C (day/night)] on flower induction. The vernalization requirements (0, 4, 8 and 12 wk at 4°C) to reflower TKS plants were also examined in controlled environments at 21/18°C. Plants harvested in September or October required 4 wk of vernalization and growth at 15/13°C to maximize the percentage of plants with flower buds and minimize the time for flower bud appearance. Those harvested in November flowered quickly and at high frequency with no vernalization and regrowth at 21/18°C. Vernalization was not essential to re-induce flowering; 80–100% of plants flowered regardless of treatment. Various combinations of harvest dates, vernalization periods and regrowth temperatures can be used to maximize flowering in TKS and have a positive impact on germplasm development.
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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".