Sustainable wheat (<i>Triticum aestivum</i>L.) production in saline fields: a review
Bibliographic record
Abstract
L.) ones, are subjected to various stresses including salinity. Given the increasing world population, finding methods and strategies that can alleviate salinity stress on crop yield production is of outmost importance. The presented review has consulted more than 400 articles related to the clean and sustainable production of wheat in saline fields affected by biological, environmental, economical, and social parameters including the important issue of climate change (global warming). The negative effects of salt stress on plant growth and the techniques, which have been so far detected to alleviate salinity stress on wheat growth have been analyzed and presented. The naturally tolerant species of wheat can use a range of mechanisms to alleviate salinity stress including sodium exclusion, potassium retention, and osmoregulation. However, the following can be considered as the most important techniques to enhance wheat tolerance under stress: (1) the biotechnological (crop breeding), biological (soil microbes), and biochemical (seed priming) methods, (2) the use of naturally tolerant genotypes, and (3) their combined use. The proper handling of irrigation water is also an important subject, which must be considered when planting wheat in saline fields. In conclusion, the sustainable and cleaner production of wheat under salt stress is determined by a combination of different parameters including the biotechnological techniques, which if handled properly, can enhance wheat production in saline fields.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".