Comparing the relationship between seed germination and temperature for<i>Stipa</i>species on the Tibetan Plateau
Bibliographic record
Abstract
Alpine steppe grasslands dominated by Stipa species (Poaceae) on the Tibetan Plateau are important model ecosystems. Here, we present data on seed germination of three typical Stipa species (Stipa purpurea Griseb., Stipa glareosa P.A.Smirn., and Stipa capillacea Keng) from the northern core region of the Tibetan Plateau. We carried out laboratory investigations of germination behavior under both constant and alternating temperatures. Germination varied significantly with temperature. Under constant temperature, we found that temperature and species, but not their interaction, had significant effects on seed germination. Under alternating temperatures, species had a significant effect on seed germination, whereas the effects of alternating temperature and the interaction between species and alternating temperature were not significant. In addition, light and the interaction of light and species had no significant effect on seed germination; however, species had a significant effect, implying that Stipa species on the Tibetan Plateau are not inhibited by light. Base temperatures of S. glareosa, S. purpurea, and S. capillacea were 1.0 °C, 0.1 °C, and –1.4 °C, respectively, with corresponding thermal times at suboptimal temperatures of 233 °C-day, 154 °C-day, and 263.2 °C-day. Our results suggest that Stipa seed germination characteristics are adaptions to a harsh environment and are species-specific.
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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".