Bibliographic record
Abstract
Interbull has introduced a new validation test, and provided corresponding software to detect non-zero time trends and outliers years, for estimates of genetic variance. The test is applied separately for cows and AI sires, for all traits included in the Interbull MACE evaluation service. In recent years, AI sires have been genomically preselected, using genotype-based evaluations when they were young calves. Genomic preselection significantly changes the expectation of Mendelian sampling distributions for AI bulls. The new Interbull test is applied to EBV computed without genotypes, which are biased by ignored genomic preselection effects. The purposes of the present study were to apply the new validation test to Canadian data, firstly using official EBV submitted for MACE, and secondly using corrected EBV, after making adjustments to reduce preselection biases in the MS distributions of the most recent AI bulls. For the main traits under selection in Canada, test results were a pass for official EBV, but a fail for bias-corrected EBV. For bull populations with genomic preselection, biased EBV are expected to pass the test, while unbiased data are expected to fail.
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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 | 0.040 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".