Chickpeas from a Chilean Region Affected by a Climate-Related Catastrophe: Effects of Water Stress on Grain Yield and Flavonoid Composition
Bibliographic record
Abstract
The Valparaiso region in Chile was decreed a zone affected by catastrophe in 2019 as a consequence of one of the driest seasons of the last 50 years. In this study, three varieties ('Alfa-INIA', 'California-INIA', and one landrace, 'Local Navidad') of kabuli-type chickpea seeds produced in 2018 (control) and 2019 (climate-related catastrophe, hereafter named water stress) were evaluated for their grain yield. Furthermore, the flavonoid profile of both free and esterified phenolic extracts was determined using liquid chromatography-mass spectrometry, and the concentration of the main flavonoid, biochanin A, was determined using liquid chromatography with diode array detection. The grain yield was decreased by up to 25 times in 2019. The concentration of biochanin A was up to 3.2 times higher in samples from the second season (water stress). This study demonstrates that water stress induces biosynthesis of biochanin A. However, positive changes in the biochanin A concentration are overshadowed by negative changes in the grain yield. Therefore, water stress, which may be worsened by climate change in the upcoming years, may jeopardize both the production of chickpeas and the supply of biochanin A, a bioactive compound that can be used to produce dietary supplements and/or nutraceuticals.
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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) | 0.000 | 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".