Robust estimation of breeding values in a random regression test‐day model
Bibliographic record
Abstract
Summary Two robust estimation procedures were applied to a random regression test‐day model to reduce the effects of test‐day records that are generally labelled as outliers. One robust procedure consisted of estimating residuals (actual observation minus predicted) from the genetic evaluation model, computing the standard deviation of residuals across all records, and restricting the outlier residuals to be within k standard deviations. Thus, a new observation is created for use in the genetic evaluation model. The process is part of the iterations on data to obtain solutions to mixed model equations until no more outliers beyond k standard deviations exist. Four different values of k were examined. The second robust procedure utilizes different weights with each test‐day record. Weights are estimated from the residuals for all observations. Outliers tend to receive smaller weights, and thus, their influence tends to be reduced. In this study, eleven different weight formulas were compared. The objectives were to apply the robust procedures to test‐day milk yield records of Canadian Jersey cattle and to determine the effects on estimated breeding values (EBVs) and rankings of animals. Results were compared to usual best linear unbiased prediction (BLUP) ignoring the outlier problem.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".