Root colonization of faba bean (<i>Vicia faba</i>L.) and pea (<i>Pisum sativum</i>L.) by<i>Rhizobium leguminosarum</i>bv.<i>viciae</i>in the presence of nitrate-nitrogen
Bibliographic record
Abstract
There is a lack of knowledge concerning the effect of nitrate-nitrogen (NO3(-)-N) at levels known to inhibit nodule formation and functioning on root colonization of dinitrogen-fixing legumes. Firstly, this study investigated potential differences between Rhizobium leguminosarum bv. viciae 175F9 and its bioluminescent-labeled strain 175F9.lux on root colonization of faba bean (Vicia faba L.) and pea (Pisum sativum L.). These two strains similarly colonized the roots of both hosts. Secondly, this study evaluated the effects of 0 and 10 mol x m(-3) NO3(-)-N on root colonization of faba bean and pea by strain 175F9.lux, over time. Averaged over both hosts and harvest dates, the presence of NO3(-)-N increased the rhizobial population and the root length colonized. In addition, our results showed that bioluminescence activity increased from 7 to 14 days after sowing and was not correlated to rhizobial population. Finally, to demonstrate that an increase in bioluminescence activity was not an indirect effect of nitrate on R. leguminosarum bv. viciae 175F9.lux, this study investigated the effects of increasing carbon (mannitol) and nitrogen (NO3(-)-N) concentrations on the rhizobial population and bioluminescence activity. The carbon source was more important than the nitrogen source to increase the rhizobial population and bioluminescence activity, which increased with increasing mannitol concentration, but not with increasing nitrate concentration. Results from this study demonstrated that NO3(-)-N increased rhizobial population, especially for faba bean, and the length of root colonized.
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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 | 0.000 | 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".