FOLIAGE APPLIED SILICON AMELIORATES DROUGHT STRESS THROUGH PHYSIO-MORPHOLOGICAL TRAITS, OSMOPROTECTANTS AND ANTIOXIDANT METABOLISM OF CAMELINA (Camelina sativa L.) GENOTYPES
Bibliographic record
Abstract
Silicon (Si) is one of the best plant defense elements against the biotic and abiotic stresses. Camelina plants accumulate Si which serves in protection against drought stress. The present study was conducted to investigate the impact of different doses of foliage applied Si (0, 3, 6 and 9 mM) under water stress (40% field capacity, FC) and non-stress conditions (100% FC) on camelina genotypes (Canadian and Australian). The imposed drought drastically decreased the growth parameters like root-shoot length and plant fresh and dry weight and also had negative impact on the chlorophyll content along with water relation attributes (water potential, osmotic potential and turgor pressure). In contrast, total free amino acids, total soluble proteins, proline and antioxidants such as ascorbic peroxidase (APX), superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) were enhanced especially in water stressed Canadian genotype, while osmoprotectants (flavonoids, anthocyanins and glycinebetaine) and phenolics contents were decreased. On the other hand, the foliar application of Si was instrumental in enhancing the growth of camelina by increasing the chlorophyll contents and water relation of stressed and non-stressed plants. Similarly, the biochemical, osmoprotectants and antioxidant metabolism was also improved in camelina stressed plants through the application of foliar Si. In conclusion, foliar application of 6 mM Si at vegetative growth stage played a vital role in alleviating the drastic impact of water stress on camelina growth by improving the water status, chlorophyll content, accumulation of phenolics and osmoprotectants and activating antioxidants. Therefore, the foliar application of Si could be developed as an important biologically viable strategy for boosting the tolerance in camelina plants to water stress conditions.
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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".