Screening some pine species from North America and dried zones of western Asia for drought stress tolerance in terms of nutrients status, biochemical and physiological characteristics
Bibliographic record
Abstract
Drought due to climate change or reduced precipitation is one of the main factors limiting the growth and establishment of plants and is one of the most critical challenges facing humans. To investigate the effect of different levels of drought stress on some pine species, this research was carried out as a factorial experiment using two factors and a completely randomized design. It included five populations of four pine species ( Pinus brutia Ten. var. eldarica, P. nigra Arnold, P. mugo , and P. banksiana Lamb (including populations 8310055 and 8960049), and three levels of irrigation (100%, 75%, or 50% FC, denoted as normal, mild or intense drought stress, respectively) with three replicates. The findings showed that, photosynthetic pigments, relative water content, visual quality, the content of nutrients, protein content, and fresh and dry weight all decreased significantly when plants were exposed to intense drought stress. However, raised proline levels, electrolyte leakage percentage, soluble sugars levels, and antioxidant enzyme activity. We detected a decline in most growth traits when comparing mild drought stress conditions to normal irrigation, yet acceptable quality seedlings when compared to intense drought stress. Intense drought stress had a substantial impact on many pine seedlings. PCA results showed that among different pine species, the level of resistance to drought is as follows: P. mugo > P. brutia var. eldarica> P. nigra > P. banksiana 8310055> P. banksiana 8960049. Our novel finding was that, P. mugo is a resistant species in arid and semi-arid regions, and P. banksiana species, especially its population of 8960049, is sensitive.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".