Variations in seed characteristics among and within <i>Stipa purpurea</i> populations on the Qinghai–Tibet Plateau
Bibliographic record
Abstract
Variation in seed size is common among plant species, populations, and individuals. On the Qinghai–Tibet Plateau, previous studies have mainly focused on interspecific variation in seed size, with little information available regarding intraspecific variation. The alpine steppe is among the most important vegetation types on the plateau, where it plays a vital role in preserving landscape heterogeneity and diversity. Stipa purpurea Griseb., endemic to the Qinghai–Tibet Plateau, is the predominant species of the alpine steppe. In the present study, we measured seed characteristics of nine S. purpurea populations and analyzed possible sources and consequences of variation in these characteristics. Seed characteristics varied greatly among and within populations. Our findings suggest that variation in seed size and awn length may affect germination and dispersal, respectively. Surprisingly, environmental factors, rather than genome size, were significantly correlated with seed size. For example, relative humidity and number of windy days were strongly correlated with seed size and awn length, respectively. We believe that variation in seed characteristics is a consequence of complex environmental conditions correlated with longitude and latitude. The results indicated that variation in seed characteristics of S. purpurea is an adaptation to environmental conditions.
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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 | 0.001 | 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".