Evaluation of Wild Lentil Species as Genetic Resources to Improve Drought Tolerance in Cultivated Lentil
Bibliographic record
Abstract
Increasingly unpredictable annual rainfall amounts and distribution patterns have far reaching implications for pulse crop biology. Seedling and whole plant survival will be affected given that water is a key factor in plant photosynthesis and also influences the evolving disease spectrum that affects crops. The wild relatives of cultivated lentil are native to drought prone areas, making them good candidates for the evaluation of drought tolerance traits. We evaluated root and shoot traits of genotypes of cultivated lentil and five wild species grown under two water deficit regimes as well as fully watered conditions over a 13 week period indoors. Plants were grown in sectioned polyvinyl chloride (PVC) tubes containing field soil from the A, B, and C horizons. We found that root distribution into different soil horizons varied among wild lentil genotypes. Secondly, wild lentil genotypes employed diverse strategies such as delayed flowering, reduced transpiration rates, reduced plant height, and deep root systems to either escape, evade or tolerate drought conditions. In some cases, more than one drought strategy was observed within the same genotype. Sequence based classification of wild and cultivated genotypes did not explain patterns of drought response. The environmental conditions at their centers of origin may explain the patterns of drought strategies observed in wild lentils. The production of numerous small seeds by wild lentil genotypes may have implications for yield improvement in lentil breeding programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".