Calculation of Weighting Factors for the Canadian Test Day Model
Bibliographic record
Abstract
Interbull has designed a new procedure to calculate weighting factors for use in international genetic evaluations. The procedure as described consists of two steps. Step 1 calculates the reliability based on an animal's own performance records. Step 2 uses this reliability on progeny and mates to calculate a weight for each bull. Although Interbull provides two alternatives for Step 1, neither method can be applied to the random regression test day model implemented in Canada. The first method given is for a single trait model, and the Canadian test day model (CTDM) is a multiple trait model. The second method uses a P matrix that is the sum of genetic and residual covariances. However in the CTDM, observations and residual covariances are for test day yields while breeding values and genetic covariances are for parameters in a curve. The genetic and residual covariances for the CTDM cannot be added since they are on different scales. Therefore, a procedure was developed to calculate reliabilities for a random regression test day model that could be used in Step 1 of the Interbull weighting factor calculation. The new procedure accounted for the same effects that were considered in the Step 1 methods presented by Interbull. The new Step 1 procedure was based on the domestic reliability calculation (Jamrozik et al., 2000) as currently implemented
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".