Can wild lentil genotypes help improve water use and transpiration efficiency in cultivated lentil?
Bibliographic record
Abstract
Abstract Climate change forecasts point to increased frequency of droughts which may affect plant growth. For protein crops such as lentil, genetic improvement of both water use and drought tolerance is necessary. Wild lentil species are known to have evolved in drought prone areas and can be introgressed into cultivated lentil, making them candidates for the evaluation of high transpiration efficiency (TE) and drought tolerance. We assessed TE, water use and drought tolerance at the plant level for five wild lentil species and in cultivated lentil. Under fully watered and moderate drought conditions, wild lentil genotypes consumed significantly less water to fix similar or more dry matter compared with their cultivated counterparts. Under severe drought conditions, the wild lentil genotype L. ervoides IG 72815 had significantly higher TE compared with L. culinaris Eston. Lens ervoides L-01-827A, had significantly higher yield compared with all other species in the presence or absence of drought and showed significantly higher (α = 5%) TE under moderate drought. Drought susceptibility index was identified as a tool to identify drought-tolerant lentil genotypes grown under severe drought. The numerous small seeds of wild lentil made it difficult to estimate drought indices that are weight based and require formulae that incorporate seed numbers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".