Physiological responses to drought in three provenances of Discorea nipponica Makino
Bibliographic record
Abstract
Dioscorea nipponica Makino is an optimal candidate to develop the diosgenin industry in North China. Given the large fluctuations in soil water availability induced by global climate change, information on drought tolerance of this species is urgently needed. Thus, seedlings of three provenances, selected from Manghe, Pangquangou nd Luyashan Nature Reserves in Shanxi Province, were exposed to 70%-85%, 55%-60%, 40%-45% and 20%-35% of water holding capacity, representing normal-watered, light drought stress, moderate drought stress and severe drought stress, respectively. Thirteen indices concerning plant water status, photosynthesis, antioxidants and osmotic regulation were recorded. Principal Component Analysis was applied to identify indices with a high contribution to drought tolerance, fulfilled by the average of subordinate function values (Xij) of drought tolerance index (Xij). We found that rhizome-propagated seedlings of D. nipponica Makino could survive eighty days of severe drought. The drought tolerance of this species is achieved mainly through physiological responses including decreased photosynthesis, increased activity of antioxidant enzymes, and accumulation of osmotic regulating compounds. The means of drought tolerance index for the provenances Manghe, Pangquangou and Luyashan were 0.29, 0.68 and 0.50, respectively. Pangquangou provenance showed higher drought tolerance than the other two, indicating that it might be a good candidate for cross breeding to combat the increasing drought climate in Shanxi Province.
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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) | 0.000 | 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".