Drought-induced changes in free amino acid and ureide concentrations of nitrogen-fixing chickpea
Bibliographic record
Abstract
The metabolic products of nitrogen (N) fixation in a legume can be amides or ureides. Chickpea (Cicer arientinum L.) is a cool-season legume which transports both ureides and amides from N fixation. Ureides are purine derivatives which are synthesized solely in the nodules. Drought-sensitive legumes accumulate ureides, but the regulation of these N fixation products during drought in chickpea is not yet known. The objectives of this study were (1) to measure the metabolic products of N fixation during drought and (2) to quantify the differences in N fixation among chickpea cultivars during drought by acetylene reduction activity under controlled environment conditions. Five chickpea cultivars were exposed to drought by soil dehydration. Plants were harvested at 1, 5, 10 and 15 d after drought and were analyzed for leaf and stem ureide concentrations and total N. In separate growth chamber experiments, N fixation was quantified daily. Leaf ureide and free amino acid concentrations were analyzed at 1, 5, and 15 d after drought. Drought increased ureide accumulation in drought-sensitive cultivars and decreased total N, alanine and asparagine concentrations over time. Drought-tolerant chickpea cultivars maintained ureide and amide concentrations during drought. Of the chickpea cultivars examined, Myles was the most drought tolerant and CDC Chico was the least. Further research on leaf ureides, alanine and asparagine concentrations would be valuable to determine if these metabolic products could serve as measures for screening chickpea germplasm for drought-tolerant N fixation. Key words: Ureides, N fixation, Asparagine, Acetylene reduction
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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 | 0.001 |
| 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".