Extension of Cut Carnation Vase Life Using Compost Tea, Putrescine and Plant Extracts
Bibliographic record
Abstract
Cut flower vase life can be extended by various natural products, but their efficacy when mixed with compost tea is not reported. A study was performed to determine cut carnation (Dianthus caryophyllus cv. White Sim) vase life in 1 L of formulations (1) C3.5: 3.5% compost tea + 15 mg putrescine; (2) R5L10: C3.5 + 5 mL rosemary (Salvia rosmarinus) + 10 mL lemon (Citrus limon) extracts; (3) R10L10: C3.5 + 10 mL rosemary + 10 mL lemon extracts; (4) R5L20: C3.5 + 5 mL rosemary + 20 mL lemon extracts; (5) R10L20: C3.5 + 10 mL rosemary + 20 mL lemon extracts; (6) Chrysal: Floral Chrysal Clear (positive control); and (7) Dw: distilled water alone (negative control). The 15 mg putrescine significantly (P=0.002) reduced compost tea turbidity by 34%. The neutral pH of C3.5 and Dw did not change. However, pH increased by 24% in Chrysal and reduced by 39% in R5L10, R10L10 and R5L20 and by 54% in R10L20 on day 11. TDS and EC were highest in Chrysal from days 1 to 11. Petal discoloration, wilt, shrinkage and neck bend were delayed by C3.5 followed by Chrysal and then R5L10, but were accelerated by the other treatments. In conclusion, the cut carnation cv. White Sim longevity as influenced by variations in the vase solutions was Chrysal (14 days) > C3.5 (12.6 days) > R5L10 (10.7 days) but ≤ 9 days in the other treatments. These results were confirmed by a 2-D principal component analysis biplot. Future study will investigate microbial interaction.
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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) | 0.002 | 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".