Does cross‐acclimation between drought and freezing stress persist over ecologically relevant time spans? A test using the grass <i>Poa pratensis</i>
Bibliographic record
Abstract
Despite evidence that prior exposure to drought can increase subsequent plant freezing tolerance, few studies have explored such interactions over ecologically relevant time spans. We examined the combined effects of drought and subsequent freezing on tiller growth and leaf sugar concentrations in the grass, Poa pratensis. We exposed tillers to no drought (-0.04 MPa), moderate drought (-0.19 MPa) or severe drought (-0.42 MPa) for 3 weeks in summer. Tillers were then frozen in autumn or spring at -5 °C (frost damage) or at 0 °C (control) for 3 days and harvested after a re-growth period. For shoot growth, there was a significant interaction between drought and autumn freezing, whereby the relative effect of freezing on growth was least for the plants previously exposed to severe drought; however, there was no significant interaction between drought and spring freezing. For root growth, there were no significant interactions between drought and freezing in either season. Leaf sugar concentrations increased significantly with drought intensity, but these effects dissipated within a month, prior to the onset of the autumn freezing treatment. Overall, our results suggest that interactions between prior drought and subsequent freezing in P. pratensis may be most relevant in the context of autumn freezing, and despite the important role of soluble sugars in increasing both drought and freezing tolerance in this species, the retention of these compounds after drought stress does not appear to explain the occurrence of drought-frost interactions at ecologically relevant time scales.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".