Seed size variation in cold and freezing tolerance during seed germination of winterfat (<i>Krascheninnikovia lanata</i>) (Chenopodiaceae)
Bibliographic record
Abstract
Native plants have adaptations to their local environments and elucidation of these traits has implications in both agronomy and restoration ecology. Winterfat ( Krascheninnikovia lanata (Pursh) A.D.J. Meeuse & Smit) is a native perennial shrub in North America capable of germinating at low temperatures. The effect of seed size on germination ability at low and subzero temperatures and the physiological mechanisms were investigated. Winterfat seeds achieved 50%–72% germination at –3 °C, a temperature slightly above the base temperature estimated using thermal time models. Small seeds required a longer time to reach 50% germination at subzero temperatures than large seeds. Large seeds maintained stable water uptake rate for both the seed and the embryo when temperatures decreased from 5 to –1 °C. In contrast, faster water uptake and greater relative K+leakage in small seeds indicated possible damage to membrane integrity at subzero temperatures. Carbohydrate conversion efficiency (Rq/RCO2) of large seeds was significantly higher than that of small seeds at 10 °C but not at 20 °C. Higher cold resistance in large seeds was also correlated with higher concentrations of glucose, raffinose, and sucrose. This study revealed the potential basis of the low-temperature germination advantage of large seeds and provided the first direct evidence of germination under freezing temperatures in winterfat.
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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".