Mycorrhizal inoculation mitigates damage from an intermediate, but not severe, frost event for a cool-season perennial bunchgrass
Bibliographic record
Abstract
Extreme cold events can damage plant tissues, altering growth and reproduction. Soil fungi may help plants tolerate environmental stressors, but the role these microbes play during episodes of severe cold warrants further examination. Using the bunchgrass Elymus canadensis L., we tested how inoculation with mycorrhizal fungi alters plant tolerance to freezing temperatures (tested at –8 °C and –16 °C). We found that, regardless of mycorrhizal inoculation, E. canadensis exposed to –16 °C exhibited greater tissue damage, less tiller growth, and fewer reproductive tillers than plants exposed to the control or –8 °C conditions. Plants exposed to –8 °C and –16 °C displayed greater levels of visible damage compared with the control plants. Mycorrhizae reduced damage to tillers in the –8 °C treatment, but had less effect on tiller damage in the control or –16 °C treatments. Inoculation with arbuscular mycorrhizal fungi limited the tiller number for E. canadensis, but only at the control temperature, suggesting that mycorrhizae may impose costs on E. canadensis under benign thermal conditions. Our study demonstrates that extreme temperatures can affect multiple components of growth in E. canadensis, and that the costs and benefits of arbuscular mycorrhizal fungi, where found, depend upon the thermal environment. Our findings reinforce the overarching importance of historically rare, but increasingly common, environmental extremes in shaping the growth of plants.
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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".