Salinity Effects on Yield and Yield Components of Contrasting Naked Oat Genotypes
Bibliographic record
Abstract
Global crop production systems are challenged by the increasing areas of saline soil in arid and semi-arid regions. Two naked oat (Avena sativa L.) lines (‘VAO-7’ and ‘VAO-24’) with distinct seedling tolerance to salinity were subjected to six levels of salt concentrations in a controlled greenhouse, and the response of yield and yield components to salinity stress was determined. The salt treatments 50, 100, 150, 200, and 250 mM sodium chloride (NaCl) (corresponding to EC: 3.42, 6.74, 9.66, 12.40, 15.04 dS m−1) imposed through modified Hoagland solution. Plain Hoagland was used as control. Complete nutrient elements were provided during the entire growth period. At maturity, the number of tillers with emerged heads was counted; the plant was then harvested and separated into shoots, seeds, and roots. Both plant height and days to maturity were shortened with increasing salt stress. Among the yield components, spikelet, tiller number, and grain dry weight per plant were significantly reduced by increasing salt concentration. Number of spikelets and grain weight per plant were the most salt-sensitive yield components. Thousand grain weight also varied as salinity stress increased. Harvest index remained relatively unchanged until the salinity reached 150 mM and higher. Our data indicate that grain yield reduction in oat due to salinity stress is associated with reduced number of grains per plant and mean grain weight.
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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".