1 Markers Assisted Selection for the Canadian Swine Industry
Bibliographic record
Abstract
The Canadian pork industry enjoys a solid reputation world wide for superior quality and health status. The Canadian pork exports have continued to increase during the recent years. Canada ranks number one among the pork exporting countries of the world. Domestically, the pork industry is also recognised as a major industry sector in agriculture, contributing to the trade surplus. The Canadian Swine Improvement Program has a proven history of genetic improvements over the past several years. Over 90,000 pigs are tested each year on the Canadian Swine Improvement Program. There are 120 participating herds across Canada and over 9000 nucleus sows. The program involves selection and genetic improvement of three major breeds: Duroc, Yorkshire and Landrace. Over 7,000 new records are added every month in the national database and genetic evaluations are computed for growth, feed efficiency, carcass traits and sow productivity. The swine improvement program is supported and operated by the Canadian Centre for Swine Improvement (CCSI) and member organisations. CCSI is a national organisation created by the swine industry to provide leadership, coordination and services relation to genetic improvement. CCSI operates a research program with industry partners, research institutions and Canadian universities. The current research plan includes integration of molecular genetics in selection programs as one of the key components along with research on selection strategies, genetic evaluation methods and optimum use of genetics by the producers.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".