Bibliographic record
Abstract
Methods: SC124 and Fuxuan 01, two cold-resistant cassava cultivars and Nanzhi199 and SC205, another two cold-sensitive cassava cultivars were studied under low temperature stress. Aims: in order to explore the hardy physiological characters of different cassava cultivars. Results: Results showed that under low temperature stress, the cell membrane permeability of the tested cassavas increased, the increase ranges in the cold-sensitive cultivars were more obvious than those in the strong cold resistance ones; the MAD contents in leaves were in a rise-fall sequence while the MAD contents in the cold-sensitive cultivars were much higher than those in the cold-resistant ones; under low temperature stress, the SOD and POD activities in leaves of cold-sensitive cultivars dropped greater than those in the strong cold-resistant ones; the contents of proline, soluble sugar and soluble liquid protein in leaves increased more seemingly in the cold-resistant cultivars than in the cold-sensitive ones. The contents of soluble sugar and soluble liquid protein in leaves were in a rise-fall trend as the temperature dropped and time elapsed. There was a dramatic decline in the chlorophyll contents and water ratios in leaves under the low temperature stress, more seemingly in the weak cold resistance cultivars than in the stronger ones. Conclusions: It was obvious that there was a close relation between the cold resistance and the cell membrane permeability, MDA contents, SOD and POD activities and the contents of proline, soluble sugar and soluble protein in different cassava cultivars. And those physiological characters could be used as indexes of cold resistance.
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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".