The roles of root‐nodulating bacterial associations and cyanogenesis in the freezing sensitivities of herbaceous legumes
Bibliographic record
Abstract
PREMISE: Reduced snow cover and increasing temperature variability can increase freezing stress for herbaceous plants in northern temperate regions. Legumes have emerged as a plant functional group that is highly sensitive to these changes relative to other herbaceous species in these regions. We explored root-nodulating bacterial associations and cyanogenesis as potential mechanisms explaining this relatively low freezing tolerance of legumes. METHODS: To examine the influence of bacterial associations, we grew four legume species with or without crushed-nodule inoculum at three severities of freezing, and three concentrations of nitrogen to disambiguate the direct benefits of increased nitrogen from the total bacterial effect. We quantified cyanogenesis via hydrogen cyanide production in both true leaves and cotyledons for nine legume species. RESULTS: Root nodulation generally only affected legume survival under low nitrogen, when freezing severity was moderate or low. However, for the frost-surviving plants, the growth advantage provided by nodulation decreased (it was often no longer significant with increasing freezing severity), and greater freezing severity reduced total nodule mass. In contrast, cyanogenesis was only detected in two of the nine species. CONCLUSIONS: The diminished performance of nodulated plants in response to freezing could place legumes at a competitive disadvantage and potentially explain their high sensitivity to freezing relative to other herbaceous species in northern temperate regions. Overall, this result has important implications for changes in soil fertility, community composition, and plant productivity in these ecosystems in the context of a changing winter climate.
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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".