Cold-adapted rhizobia for nitrogen fixation in temperate regions
Bibliographic record
Abstract
Rhizobia from Canadian soils were selected for cold adaptation with the aim of improving productivity of legumes that are subjected to cool temperatures during the growing season. One approach was to use rhizobia associated with legume species indigenous to arctic and subarctic regions: (i) Mesorhizobium sp. isolated from Astragalus and Oxytropis spp. and (ii) Rhizobium leguminosarum from Lathryrus spp. The majority of these rhizobia are considered psychrotrophs because they can grow at 0 °C. The advantages of cold adaptation of arctic Mesorhizobium to improve legume symbiosis were demonstrated with the temperate forage legume sainfoin (Onobrychis viciifolia). In laboratory and field studies, arctic rhizobia were more efficient than temperate (commercial) rhizobia in improving growth of sainfoin and were more competitive in forming nodules. Biochemical studies on cold adaptation showed higher synthesis of cold shock proteins in cold-adapted than in nonadapted arctic rhizobia. Since arctic Mesorhizobium cannot nodulate agronomically important legumes, the nodulation genes and the bacterial signals (Nod factors) were characterized as a first step to modifying the host specificity of nodulation. Another valuable approach was to screen for cold adaptation, that is, rhizobia naturally associated with agronomic legumes cultivated in temperate areas. A superior strain of Sinorhizobium meliloti adapted for nodulation of alfalfa at low temperatures was selected and was the most efficient for improving growth of alfalfa in laboratory and field studies. This strain also performed well in improving regrowth of alfalfa after overwintering under cold and anaerobic (ice encasement) stresses, indicating a possible cross-adaptation of selected rhizobia for various abiotic stresses inherent to temperate climates.Key words: cold adaptation, legumes, symbiotic efficiency, cold shock protein, nodulation genes, anaerobiosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".