Effects of High Temperature and Waterlogging Stress on Cellulase Activity of Non-heading Chinese Cabbage
Bibliographic record
Abstract
Two non-heading Chinese cabbage varieties 'Heiyoudong' and 'Suzhouqing' were used as materials to study the effects of high temperature and waterlogging stress on the activities of enzymes related to cellulose synthesis in non-heading Chinese cabbage. The two varieties had different resistance to high temperature and waterlogging. Three temperature gradients of 24℃, 35℃ and 40℃ were set, and each temperature was set two treatments, waterlogging and non-waterlogging. The results showed that under high temperature stress, the CesA, SS, and KOR enzyme activities of 'Heiyoudong' and 'Suzhouqing' showed a trend of first increasing and then decreasing, while the trend of CE enzyme activity was opposite. Under the combined stress of high temperature and waterlogging, the changed trend of the enzyme activities related to cellulose synthesis in 'Heiyoudong' and 'Suzhouqing' is similar to that of a single high temperature stress, but the degree of impact is greater than that of a single high temperature stress. In addition, regardless of high temperature stress, high temperature and waterlogging combined stress, the increase in CesA, SS, and KOR activities in 'Heiyoudong' was greater than that of 'Suzhouqing', but the decrease was less than that of 'Suzhouqing'; The increase in CE activity of 'Suzhouqing' is less than that of 'Suzhouqing', but the decrease rate is greater than that of 'Suzhouqing'.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".