Impact of seasonally frozen soil on germinability and antioxidant enzyme activity of <i>Picea asperata</i> seeds
Bibliographic record
Abstract
In the subalpine zone of the Qinghai–Tibetan Plateau of China, Dragon spruce (Picea asperata Mast.) is commonly used for reforestation. The aim of the present work was to study the effects of seasonally frozen soil on the germination of P. asperata seeds and to investigate whether these effects were associated with resumption of the antioxidant defense system. The nonfrozen treatment resulted in near failure of germination (1%) and was associated with relatively high levels of hydrogen peroxide (H2O2) and low activities of superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxide (APX). Germination of P. asperata seeds at 10 cm under the seasonally frozen soil was higher than that at 5 cm by 26%; this higher germination rate was associated with the recovery of SOD, CAT, and APX activities. The levels of malondialdehyde (MDA) in seeds from seasonally frozen treatments were higher than those in the nonfrozen treatment, implying greater lipid peroxidation and that frozen seeds might have suffered from oxidative stress. The results indicate that seasonally frozen soil facilitated the germination of P. asperata seeds and that germination was closely related to the resumption of antioxidant enzymes activity. Overall, these findings suggest that the disappearance of seasonally frozen ground caused by global warming might result in failure of regeneration of P. asperata.
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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".