Changes in Crested Wheatgrass Root Exudation Caused by Flood, Drought, and Nutrient Stress
Bibliographic record
Abstract
Root exudates can chelate inorganic soil contaminants, change rhizosphere pH, and may increase degradation of organic contaminants by microbial cometabolism. Root-zone stress may increase exudation and enhance phytoremediation. We studied the effects of low K+, high NH4+/NO3- ratio, drought, and flooding on the quantity and composition of exudates. Crested wheatgrass (Agropyron cristatum) was grown in Ottawa sand in sealed, flow-through glass columns under axenic conditions for 70 d. Root exudates were collected and analyzed for total organic carbon (TOC) and organic acid content to compare treatment effects. Plants in the low K+ treatment exuded 60% more TOC per plant per day (p = 0.01) than the unstressed control. Drought stress increased cumulative TOC exuded per gram dry plant by 71% (p = 0.05). The flooded treatment increased TOC exuded per gram dry plant by 45%, although this was not statistically significant based on the two replicate plants in this treatment. Exudation from the high NH4+/NO3- ratio treatment was 10% less than the control. Exudation rates in this study ranged from 8 to 50% of rates in four other published studies. Gas chromatography-mass spectrometry (GC-MS) analysis indicated that malic acid was the predominant organic acid exuded. Fumaric, malonic, succinic, and oxalic acids were also detected in the exudates of all treatments. These results demonstrate that nutrient and water stress have significant effects on the quantity and composition of root exudates. Cultural manipulations to induce stress may change the quantity of root exudates and thus increase the effectiveness of phytoremediation.
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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".