Genetic variation and correlation of agronomic traits in meadow bromegrass (Bromus riparius Rehm) clones
Bibliographic record
Abstract
Meadow bromegrass (Bromus riparius Rehm.) is a recently introduced pasture grass in western Canada. Its leafy production and rapid regrowth have made it a major grass species for pasturing beef animals in this region. As relatively little breeding work has been done on this species, there is little information on its breeding behaviour. The main objective of this study was to estimate total genetic variability, broad-sense heritability, phenotypic and genetic correlations. Forty-four meadow bromegrass clones were evaluated for agronomic characters. Genetic variation for dry matter yield, seed yield, fertility index, harvest index, plant height, plant spread, crude protein, neutral detergent fiber and acid detergent fiber, was significant. Broad-sense heritability estimates exceeded 50% for all characters. Heritability estimates were at least 3.5 times greater than their standard errors. Phenotypic and genetic correlation between all possible characters were measured. There was general agreement in both sign and magnitude between genetic and phenotypic correlations. Correlations between the different characters demonstrated that it is possible to simultaneously improve seed and forage yield. Based on the results, it appears that the development of higher yielding cultivars with higher crude protein, and lower acid and neutral detergent fibers concentration should be possible.
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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 | 0.001 |
| 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 | 0.000 | 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".