Increasing diversity among <i>Lens</i> species for improving biological nitrogen fixation in lentil
Bibliographic record
Abstract
Abstract Exotic germplasm is a key resource for reintroducing genetic variability into cultivars. We evaluated 36 accessions from cultivated lentil (Lens culinaris Medik.) and six related wild species inoculated with a commercial strain of Rhizobium leguminosarum bv. viciae under greenhouse conditions. The objective was to explore Lens species and/or accessions that can contribute higher biological nitrogen fixation ability to the lentil crop. A split plot design was used with either Rhizobium inoculation, added nitrogen (+N), or neither as the main plots, and accessions in subplots randomized in blocks. Two repeats of the experiment were evaluated at flowering for N fixation and nodulation characters, and two subsequent experiments, with a subset of 14 accessions, were evaluated at maturity for seed production, seed quality, and harvest index. Differences in phenotypic expression corresponded to specific accessions but not to any particular Lens species. CDC Greenstar had the highest N fixation among lentil cultivars and also had superior yield results compared to the added N treatment. When inoculated, wild accessions, including IG 72643 (Lens orientalis), displayed unique and multiple desirable characteristics compared to cultivars, including indeterminate nodulation, higher N translocation, stable yield compared to added N treatment, and exceptionally high protein concentration in seeds.
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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 | 0.000 |
| 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".