Prevalence and consequences of chromosomal abnormalities in Canadian commercial swine herds
Bibliographic record
Abstract
BACKGROUND: Structural chromosome abnormalities are well known as factors that reduce fertility rate in domestic pigs. According to large-scale national cytogenetic screening programs that are implemented in France, it is estimated that new chromosome abnormalities occur at a rate of 0.5 % in fertility-unproven boars. RESULTS: This work aimed at estimating the prevalence and consequences of chromosome abnormalities in commercial swine operations in Canada. We found pig carriers at a frequency of 1.64 % (12 out of 732 boars). Carrier pigs consistently showed lower fertility values. The total number of piglets born for litters from carrier boars was between 4 and 46 % lower than the herd average. Similarly, carrier boars produced litters with a total number of piglets born alive that was between 6 and 28 % lower than the herd average. A total of 12 new structural chromosome abnormalities were identified. CONCLUSIONS: Reproductive performance is significantly reduced in sires with chromosome abnormalities. The incidence of such abnormal sires appears relatively high in populations without routine cytogenetic screening such as observed for Canada in this study. Systematic cytogenetic screening of potential breeding boars would minimise the risk of carriers of chromosome aberrations entering artificial insemination centres. This would avoid the large negative effects on productivity for the commercial sow herds and reduce the risk of transmitting abnormalities to future generations in nucleus farms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".