Bibliographic record
Abstract
The dependency of agriculture on nitrogen fertilizer inputs is associated with adverse effects on the environment and human health. The importance of biological nitrogen fixation by rhizobia in symbiotic association with legumes is underscored by its potential to reduce or replace chemical fertilizer inputs. This paper reviews research on the diversity of the symbiotic rhizobia resident in Canadian soils. Research has focussed on phenotypic and genotypic variation (e.g., nitrogen fixing efficacy, nodulating competitiveness, host range, adaptation to cool climate) within rhizobial species with the objective of selecting efficient strains for use in inoculants for legume crops. The genetic diversity of rhizobia resident in Canadian soils has been reported only for Sinorhizobium meliloti, Rhizobium leguminosarum and Mesorhizobium spp. There is a need for further studies on populations of other rhizobial species, particularly those associated with native legumes. Exploiting the diversity present in natural soil populations via selection and genetic manipulation should permit the development of superior strains for use in legume inoculants. Other rhizobial traits that may be exploited include plant growth-promoting activity and ability to degrade pollutants. Key words: Symbiotic rhizobia, diversity, Canadian soils
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".