Selection of rhizobial strains differing in their nodulation kinetics under low temperature in four temperate legume species
Bibliographic record
Abstract
Abstract Background Winter climate change including frequent freeze‐thaw episodes and shallow snow cover will have major impacts on the spring regrowth of perennial crops. Non‐bloating perennial forage legume species including sainfoin, birdsfoot trefoil, red clover, and alsike clover have been bred for their adaptation to harsh winter conditions. In parallel, the selection of cold‐tolerant rhizobial strains could allow earlier symbiotic nitrogen (N) fixation to hasten spring regrowth of legumes. Methods To identify strains forming nodules rapidly and showing high N‐fixing potential, 60 rhizobial strains in association with four temperate legume species were evaluated over 11 weeks under spring soil temperatures for kinetics of nodule formation, nitrogenase activity, and host yield. Results Strains differed in their capacity to form efficient nodules on legume hosts over time. Strains showing higher nitrogenase activity were arctic strain N10 with sainfoin and strain L2 with birdsfoot trefoil. For clovers, nitrogenase activity was similar for control and inoculated plants, likely due to formation of effective nodules in controls by endophyte rhizobia present in seeds. Conclusions Selection based on nodulation kinetics at low temperature, nitrogenase activity, and yield was effective to identify performant rhizobial strains for legume crops. The use of cold‐tolerant strains could help mitigate winter climatic changes.
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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 | 0.001 | 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".