Simulated drought with Polyethylene-Glycol (PEG) decreases above-ground performance and increases nodulation in the legume <i>Medicago lupulina</i>
Bibliographic record
Abstract
Abstract Under climate change, understanding how plants and crops respond to drought is essential for basic research in ecology and evolution, and improving agricultural resilience. One common method of simulating drought in experimental conditions is by applying polyethylene glycol (PEG) to plants. We investigated drought growth responses in Medicago lupulina (black medic) using PEG to simulate drought stress. We grew Medicago lupulina plants inoculated with Sinorhizobium meliloti in Magenta boxes under controlled conditions and randomly assigned them to one of three treatments: a control, PEG applied to the bottom (PEG added to the bottom-watering container of a magenta box), or PEG applied from the top (PEG poured over the growth media). After 60 days, we measured true leaf number, nodule count, and below- and above-ground dry biomass. PEG treatments significantly reduced above-ground growth, including total biomass and leaf number, but unexpectedly increased nodulation. Our results suggest that while PEG effectively simulates drought stress on above-ground growth parameters, it may not accurately simulate drought effects on rhizobial symbiosis. PEG treatments had no effect on below-ground biomass, suggesting that increased nodulation is not a result of increased plant investment in below-ground growth under simulated drought. We hypothesize that PEG, as a persistent liquid that plants do not absorb, created conditions favorable for nodulation. Overall, these results highlight the importance of interpreting PEG-simulated drought experiments with caution when assessing mutualistic interactions.
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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) | 0.001 | 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".