Bibliographic record
Abstract
Since the implementation of genomic evaluations in 2009, Canadian Dairy Network (CDN) has used SNP genotypes to verify the reported parents if genotyped and, when missing or incorrect, to discover the animal's sire and/or dam based on all existing SNP genotypes. To date, for breeds with official genomic evaluations in Canada, CDN has over 1.4M genotypes including 1.2M Holstein, 163,000 Jersey, 29,000 Brown Swiss, 6,000 Ayrshire and 3,000 Guernsey. These genotypes involve 23 different genotype panels, including low (3K-30K), medium (44K-140K) and high (over 600K) density. For parentage analysis, a list of 2,683 SNP in common from the 3K and 50K genotype panels are used as the basis for parentage verification, parentage discovery and for identifying families of genetically identical animals. Using the list of SNP proposed for inclusion in GenoEx-PSE for parentage verification (200) and parentage discovery (additional 675 or 354), it was concluded that the 200 SNP recommended by ISAG for parentage verification performed very well compared to the SNP routinely used by CDN for dairy cattle breeds in Canada. It was also concluded that parentage discovery using either set of additional 675 or 354 SNP also provided accurate results. To avoid a possible misuse of the additional SNP for parentage discovery, the reduced set of 354 SNP, selected from only 10 chromosome, are recommended for GenoEx-PSE due to the higher level of imputation error and lower accuracy of GEBV estimation compared to results based on the additional 675 SNP.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.114 | 0.022 |
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".