Maternal source affects seed germination of a rare Arabian desert species (<i>Astragalus sieberi</i>)
Bibliographic record
Abstract
Understanding variation of seed germination in wild populations can assist restoration projects through improved seed source selection. Recruitment of artificially distributed seed can be improved by selecting for suitable dormancy and germination characteristics. We investigated seed germination and emergence responses of three Astragalus sieberi DC. (Fabaceae) populations (Abdali, Liya, and Salmi) to in situ storage for five months at depths of 0 and 5 cm, pre-sowing hydration for 12 and 24 hours, and chemical scarification with concentrated sulfuric acid for 5, 10, and 15 min. The germination percentage was low for freshly collected seed (<3%) from Abdali and Salmi, though in situ storage partially enhanced germination. Pre-sowing hydration did not affect seedling emergence, but acid scarification effectively broke seed dormancy. The longest scarification treatment of 15 min produced the greatest seedling emergence, though populations responded differently. Our results indicate that selection of maternal, native seed sources can improve revegetation projects in desert areas. Knowledge of inter-population variation can improve the understanding of the mechanisms regulating seed germination, thus optimizing restoration projects by selecting optimal seed sources.
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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) | 0.001 | 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".