Increased metabolic potential of Rhizobium spp. is associated with bacterial competitiveness
Bibliographic record
Abstract
Of 105 rhizobial isolates obtained from nodules of commonly cultivated legumes, we selected 19 strains on the basis of a high rate of symbiotic plant growth promotion. Individual strains within the species Rhizobium leguminosarum bv. trifolii, R. leguminosarum bv. viciae, and Rhizobium etli displayed variation not only in plasmid sizes and numbers but also in the chromosomal 16S-23S internal transcribed spacer. The strains were tagged with gusA gene and their competitiveness was examined in relation to an indigenous population of rhizobia under greenhouse conditions. A group of 9 strains was thus isolated that were competitive in relation to native rhizobia in pot experiments. Nineteen selected competitive and uncompetitive strains were examined with respect to their ability to utilize various carbon and energy sources by means of commercial Biolog GN2 microplate test. The ability of the selected strains to metabolize a wide range of nutrients differed markedly and the competitive strains were able to utilize more carbon and energy sources than uncompetitive ones. A major difference concerned the utilization of amino and organic acids, which were metabolized by most of the competitive and only a few uncompetitive strains, whereas sugars and their derivatives were commonly utilized by both groups of strains. A statistically significant correlation between the ability to metabolize a broad range of substrates and nodulation competitiveness was found, indicating that metabolic properties may be an essential trait in determining the competitiveness of rhizobia.
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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 | 0.001 |
| 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) | 0.001 | 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".