Symbiosis of selected <i>Rhizobium leguminosarum</i> bv. <i>viciae</i> strains with diverse pea genotypes: effects on biological nitrogen fixation
Bibliographic record
Abstract
Biological nitrogen fixation (BNF) can be improved by optimizing the interaction between the rhizobial inoculant and pea (Pisum sativum L.), leading to increased productivity and reduced nitrogen (N) fertilizer use. Eight Rhizobium leguminosarum bv. viciae strains were used to inoculate the super-nodulating pea mutant Rondo-nod3 (fix+), the hyper-nodulating pea mutant Frisson P88 Sym29, CDC Meadow commercial control, and the non-nodulating mutant Frisson P56 (nod–) to evaluate BNF in a greenhouse assay. Significant differences in strain × cultivar interactions were detected for shoot and root dry masses, which ranged from 1.8 to 4.7 g and from 0.27 to 0.73 g per plant, respectively; for nodule number on lateral roots, which ranged from 25 to 430 per plant; for amount of fixed N2, which ranged from 15 to 67 mg and from 4 to 15 mg per plant for shoot and root tissues, respectively; and for percentage of N derived from atmosphere (%Ndfa), which ranged from 37% to 61% and from 35% to 65% for shoot and root tissue, respectively. Strain × cultivar interactions in this study could contribute to identification of superior strains and pea breeding lines with genetic superiority in BNF. Nodule production in pea plants was not necessarily correlated with the amount of fixed N2, suggesting nodule activity is more important to BNF than is nodule number.
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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) | 0.000 | 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".