Genetic variation in an orchardgrass population promises successful direct or indirect selection of superior drought tolerant genotypes
Bibliographic record
Abstract
Abstract Improvement in drought tolerance is an important component of forage grass breeding. To assess the potential of selecting drought tolerant genotypes of orchardgrass, a polycross population was created in 2010 and evaluated in the field under normal and drought stress conditions during 2011–2013. Drought stress reduced performance in forage yield, growth characteristics, and most of the physiological traits measured, but increased carotenoid content, proline content, and the chlorophyll a/b ratio. High estimates of narrow‐sense heritability for chlorophyll and carotenoid content, as well as forage yield components, indicated that phenotypic selection would be successful in achieving genetic progress. Indirect selection to improve forage yield under drought stress conditions was efficient through selecting for chlorophyll a, chlorophyll b, total chlorophyll and carotenoid content. These physiological traits were also significantly associated with drought tolerance index. Overall, families 5, 7, 8, 13, 14 and 24 with high stress tolerance index values and high forage yield under both water conditions were identified as suitable families for breeding drought adaptive varieties.
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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 | 0.000 | 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) | 0.002 | 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".