Evaluation of growth and nitrogen fixation of pea nodulation mutants in western Canada
Bibliographic record
Abstract
Optimized biological nitrogen fixation (BNF) in pea (Pisum sativum L.) could increase crop productivity and reduce nitrogen (N) fertilizer use in western Canada. We tested the BNF capabilities and growth of three pea nodulation mutants [Frisson P64 Sym29, Frisson P88 Sym28, and Rondo-nod3 (fix+)] compared with check cultivars [CDC Dakota, CDC Meadow, Frisson, Rondo, and non-fixing negative control Frisson P56 (nod–)] under field conditions in Saskatchewan, Canada, in three environments. CDC Meadow and CDC Dakota produced greater dry biomass and seed yield but less fixed N compared with the mutants. On average, Frisson P88 Sym29 fixed 19% and 31% more N per plot compared with CDC Dakota and CDC Meadow, respectively. Rondo-nod3 (fix+) fixed 12% and 23% more N per plot compared with CDC Dakota and CDC Meadow, respectively. All lines grown at Saskatoon in 2015 had longer time to flowering, greater biomass, and greater grain yield but less amounts of N fixation compared with these lines grown at Saskatoon in 2014 or Floral in 2015. Compared with the commercial checks, Frisson P88 Sym29 and Rondo-nod3 (fix+) had a high percent N derived from the atmosphere and good nodulation under relatively high soil available N content, while requiring at least one week shorter growing period to reach maturity, indicating that these mutants have potential as parents in breeding for improved BNF in pea.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".