Analysis of genetic diversity in four Canadian swine breeds using pedigree data
Bibliographic record
Abstract
Conservation of animal genetic resources entails judicious assessment of genetic diversity as a first step. The objective of this study was to analyze the trend of within-breed genetic diversity and identify major causes of loss of genetic diversity in four swine breeds based on pedigree data. Pedigree files from Duroc (DC), Hampshire (HP), Lacombe (LC) and Landrace (LR) containing 480 191, 114 871, 51 397 and 1 080 144 records, respectively, were analyzed. Pedigree completeness, quality and depth were determined. Several parameters derived from the in-depth pedigree analyses were used to measure trends and current levels of genetic diversity. Pedigree completeness indexes of the four breeds were 90.4, 52.7, 89.6 and 96.1%, respectively. The estimated percentage of genetic diversity lost within each breed over the last three decades was approximately 3, 22, 12 and 2%, respectively. The relative proportion of genetic diversity lost due to random genetic drift in DC, HP, LC and LR was 74.5, 63.6, 72.9 and 60.0%, respectively. The estimated current effective population size for DC, HP, LC and LR was 72, 14, 36 and 125, respectively. Therefore, HP and LC have been found to have lost considerable genetic diversity, demanding priority for conservation. Key words: Genetic drift, effective population size
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".