Parent-offspring regression in meadow bromegrass (<i>Bromus riparius</i> Rehm.): Evaluation of two methodologies on heritability estimates
Bibliographic record
Abstract
To determine the nature and extent of inflation of estimates of heritabilities by parent-offspring regression methods, 40 clones of meadow bromegrass (Bromus riparius Rehm.) and their half-sib progenies were studied in completely randomized block design trials, with six replications in Saskatoon and Melfort, Canada. Clones and progenies were evaluated for dry matter yield, seed yield, plant height, fertility index and harvest index. The results of the analysis showed a consistent inflation of heritability estimates derived from the simple parent-offspring regression, when compared to the regression estimate by variance-covariance analysis. The two methods successfully removed the environmental covariances from the estimates. However, in the simple regression analysis, error covariance was not removed from the numerat or; therefore, heritabilities estimated by this methodology were higher than those estimated by the variance-covariance method. It was concluded that estimates derived from variance-covariance analysis provide less biased estimates of heritability. Key words: Regression analysis, heritability, meadow bromegrass
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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 | 0.003 | 0.004 |
| 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".