Effect of seed coat mucilage, temperature, and photoperiod on germination of four Arabian desert species
Bibliographic record
Abstract
In this study, we assessed the effects of mucilage presence, thermoperiod, and photoperiod on seed germination percentage and germination velocity (mean germination time) of four Arabian desert species. We hypothesized that mucilage presence, thermoperiod, and photoperiod and their interaction would influence seed germination. Seeds with and without mucilage were germinated under different alternating temperature regimes of 15/25, 20/30, and 25/35 °C night/day temperatures with a 12/0 h light/dark photoperiod. Results showed that the incubation temperature affected the germination of all the studied species. The photoperiod significantly affected the germination of Boerhavia elegans Choisy, Salvia aegyptiaca L., and Sporobolus ioclados (Trin.) Nees, while the mucilage presence influenced the germination of S. ioclados only. The interaction between mucilage presence, temperature, and photoperiod significantly influenced the germination percentage of B. elegans and S. aegyptiaca. Neither removal of the mucilage nor light conditions affected the germination percentages of Sporobolus spicatus (Vahl) Kunth. However, the temperature affected the germination of S. spicatus. The germination of all the studied species was faster for demucilaged seeds. Ecologically, the mucilaginous seed coat is considered an important adaptation for dispersal as it anchors seeds on the ground and holds water around the seed during the germination stage in the stressful arid desert habitats.
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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) | 0.000 | 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".