Rhizosphere can enhance effectiveness of rhizobia
Bibliographic record
Abstract
Annual legumes are usually inoculated with rhizobia annually to ensure high nodulation, nitrogen fixation and crop yields. To assess the potential of establishing a highly effective population of soil rhizobia in a crop preceding the legume, the effect of passing the rhizobia through crop rhizosphere prior to planting peas was investigated in pot experiments. We inoculated pea seeds with soil obtained from (a) the rhizosphere of peas or wheat with or without rhizobial inoculation, and grown in potted soil, or (b) inoculated and uninoculated soil-only control pots (non-rhizosphere). Seeds and soil had been inoculated with Rhizobium leguminosarum bv. viceae strain NRG480 (also known as 128C56G, LiphaTech, Milwaukee, WI). Inoculation of peas with previously-inoculated soil sampled from wheat rhizosphere nor pea rhizosphere had no significant effect on nodulation and plant dry matter (DM) of peas. However, inoculating peas with previously-uninoculated soil (i.e., indigenous rhizobia) sampled from pea rhizosphere significantly increased pea nodule and shoot DM. Although wheat rhizosphere did not affect nodulation and nitrogen fixation by pea rhizobia, it supported a large population of inoculated Rhizobium. These results indicate the possibility of establishing a large population of Rhizobium for a legume crop by inoculating the preceding cereal crop in a rotation.
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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 | 0.000 | 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".