International Genomic Cooperation - North American Perspective
Bibliographic record
Abstract
Centers in USA and Canada (AIPL-USDA, Animal Research Services, Beltsville, Maryland and the Canadian Dairy Network, Guelph, Ontario) have been sharing genotypes in order to enhance genomic evaluation procedures in both countries. Although genotypes and genomic methodology are shared, no direct comparison has been made between genomic breeding values published on the two country scales. January 2010 official proofs, direct genomic values and traditional EBV (termed PTA in USA) from both countries were compared. In general, genomic parent averages for young bulls were more correlated across countries than parent averages using traditional evaluations only. However, for LPI / Net Merit and Conformation, correlations across countries for first crop bulls and cows were stronger for traditional breeding values than for genomic breeding values. Accuracy of predicting future proofs was greater when all North American bulls were included in the SNP estimation, compared to only having domestic bull proofs available, with both countries realizing similar levels of accuracy. Gain in published reliability with inclusion of genomic information was greatest for young bulls and heifers evaluated in the USA, but this is also a function of differing methods used rather than differing levels of accuracy achieved with genomics. Proven bulls gained more published reliability in their second country of proof since the traditional EBV in that country would be a MACE evaluation of lower reliability so more gain is possible through genomics. This report summarizes the current benefits of collaboration in North America and challenges still to face.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".