Postharvest handling and vase life of cut sunflower
Bibliographic record
Abstract
The sunflower ( Helianthus annuus L.) is a commercially important cut flower requiring research into postharvest factors such as recutting, water uptake, stem number, cooling rate, and storage temperature to maximize vase life. Stems of 'Sunbright' sunflowers were either recut before or after a drying period up to 48 h. Water uptake, stem quality, and microbial counts were determined 4 days after rehydration. Water potential was determined on five corresponding leaves per treatment. The effects of stem number per vase were evaluated with either 1, 3, 5, or 10 stems. Effects of cooling rate were determined by temperatures of 5 °C for 3 days in the dark, 5 °C for 2 days in the dark preceded by 24 h at either 20 °C with light or 32 °C in a shaded area outdoors, or were maintained in a shaded area at 32 °C for 3 days. Hydration and storage temperature effects were determined by either transferring to a postharvest environment, 20 °C cooler for 2 h, immediate storage for 2 days at 5 °C, or 3 days at 5 °C after recutting the basal stem. Allowing stems to dry up to 48 h reduced vase life by 2.3 days or less. Vase life was unaffected by harvesting into water or by the number of stems in a vase but was affected by temperature such that the longest vase life of 13.2 days occurred when stems were stored for 3 days at 5 ± 0.5 °C followed by postharvest evaluation at 20 ± 1 °C.
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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".