Substrates and Temperatures in the Germination of Hibiscus sabdariffa L. Seeds
Bibliographic record
Abstract
Vegetable biodiversity in Brazil accounts for almost 67% of the plants on the planet, which are part of studies with focus on determinants of food security and access to adequate and healthy food, while preserving natural resources. Studies indicate that Hibiscus sabdariffa L. is one of the most studied unconventional food crops because of its nutraceutical quality as antioxidant and prevention of hypertension and cancer. Although relevant, substrate and temperature information for seedling production of this species are scarce. Thus, the aim of this study was to evaluate the best substrate and temperature for the vigor and germination test of H. sabdariffa seeds of the Talo Roxo and Rubra varieties. The experiment was laid out in a randomized complete design, in a 7 × 5 factorial arrangement, consisting of the combinations of seven substrates (paper towel, filter paper, vermiculite, between sand, on sand, plantmax® and compost) with five temperatures (20, 25, 30, 35 and 20-30 °C), with four replicates with 50 seeds each. The following variables were evaluated: germination, germination speed index, seedling length and dry mass. For germination and vigor tests of Hibiscus sabdariffa var. Talo Roxo seeds the paper roll substrate is recommended at temperatures of 25 or 35 °C and for var. Rubra seeds, plantmax® at 25 °C and plantmax® and vermiculite at temperatures of 25 or 20-30 °C, respectively.
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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 | 0.001 | 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".