Developing a Rhizobial Inoculum for Phaseolus vulgaris to increase biological nitrogen fixation using adaptive evolution
Bibliographic record
Abstract
Phaseolus vulgaris, also known as dry-bean, is an important legume that is grown in North America as a major source of dietary protein. Rhizobium etli is gram negative bacterium that is capable of entering into a symbiotic relationship with Phaseolus vulgaris providing the plant with a source of biologically reduced nitrogen. However, beans that are grown as a crop in Canada are typically fertilized using industrially derived nitrogen. This thesis describes that adaptive evolution can be used as a technique to develop inoculum strains that can be used with varieties of bean currently grown in Canada. R. etli CFN42 is a sequenced strain that is being used as a model organism to study plant-microbe interactions and nitrogen fixation. Although it interacts with current bean cultivars, it does not form an effective symbiotic association. Following an initial inoculation onto Phaseolus vulgaris cv Envoy, only small, white ineffective nodules that did not fix nitrogen were observed. These nodules were harvested and used as an inoculum on a new set of plants. The results suggest that repeated cycling of nodules can lead to the isolation of effective strains. Our data show that R. etli CFN42 initially produced nodules that were white and found on the peripheral roots, whereas strains that were isolated by cycle 3-4 were capable of developing crown nodules. Repeated cycling of these nodules led to strains that appear to produce pink and effective nodules by our 8th inoculation cycle. The majority of the isolated strains had faster growth rates with doubling times of about 5.3 0.6 hours whereas CFN42 had a doubling time of 8.7 0.6 hours. This effectiveness was reflected both in the dry weights of the plants, as well as the quantitation of nitrogen fixation by acetylene reduction.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".