Quinoa: A Promising Crop for Resolving the Bottleneck of Cultivation in Soils Affected by Multiple Environmental Abiotic Stresses
Bibliographic record
Abstract
Willd.) has gained worldwide recognition for its nutritional values, adaptability to diverse environments, and genetic diversity. This review explores the current understanding of quinoa tolerance to environmental stress, focusing on drought, salinity, heat, heavy metals, and UV-B radiation. Although drought and salinity have been extensively studied, other stress factors remain underexplored. The ever-increasing incidence of abiotic stress, exacerbated by unpredictable weather patterns and climate change, underscores the importance of understanding quinoa's responses to these challenges. Global gene banks safeguard quinoa's genetic diversity, supporting breeding efforts to develop stress-tolerant varieties. Recent advances in genomics and molecular tools offer promising opportunities to improve stress tolerance and increase the yield potential of quinoa. Transcriptomic studies have shed light on the responses of quinoa to drought and salinity, yet further studies are needed to elucidate its resilience to other abiotic stresses. Quinoa's ability to thrive on poor soils and limited water resources makes it a sustainable option for land restoration and food security enterprises. In conclusion, quinoa is a versatile and robust crop with the potential to address food security challenges under environmental constraints.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".