Root Traits, Nodulation and Root Distribution in Soil for Five Wild Lentil Species and Lens culinaris (Medik.) Grown under Well-Watered Conditions
Bibliographic record
Abstract
The efficient use of resources such as water and nutrients by plants is increasingly important as the world population food demand continues to grow. With the increased production of lentil in the temperate zones of North America, improvement in yield needs to be maintained. The use of wild lentil genotypes as sources of genetic diversity for introgression into cultivated lentil is an important breeding strategy, but little is known about their root systems. We evaluated the root systems of five wild lentil species and Lens culinaris under fully watered conditions. Plants were grown in 60 cm tubes containing equal volumes of soil collected from the reconstructed A, B and C horizons. Significant differences were observed for root traits and fine root distribution between and within species and the proportion of root biomass partitioned into each soil layer was unique for each genotype. We also observed variability in nodule number and nodule shape within and between genotypes. Some genotypes more efficiently used water for either biomass or seed production. The allocation of resources to seed production also varied between genotypes. These observations could have impact on the design of future lentil breeding in the context of strategies for managing changes in rainfall amount and distribution for lentil production ecosystems.
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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) | 0.000 | 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".