PSVII-39 Late-Breaking Abstract: Enhancing production and Aleutian disease resilience in mink through advanced genomics
Bibliographic record
Abstract
Abstract The fur industry is one of the oldest and the most historically significant industries in Canada. The industry has used American mink (Neovison vison) as the major source of fur for decades because of their high-quality fur and wide range of colours. This project will seek to (1) create the first accurate whole-genome sequence assembly of mink using next-generation sequencing technology to help understanding the biology and evolution of the order Carnivora, (2) design a robust and informative SNP assay for genomics discovery in mink, (3) discover genome structure and signature of selection as well as identify new genetic variants explaining variation in economically important traits, and (4) identify the genetic relationships among these traits including feed efficiency, Aleutian disease resilience, fur quality, reproductive performance, growth rate and pelt size. One hundred mink DNA samples from the Canadian Centre for Fur Animal Research at Dalhousie Agriculture Campus (Truro, Nova Scotia), and one breeding population (Millbank Fur Farm Limited, Rockwood, Ontario) were sequenced using next-generation whole-genome sequencing with more than 30x coverage to create the first SNP assay for American mink. A DNA panel composed of these sequenced mink from five color-types were assembled to identify the most homozygous individual as the reference animal for whole-genome sequence assembly development. The phenotypic data and DNA samples from 3,323 animals were collected and will be genotyped using the customized assay. The ultimate objective is to develop new tools for implementation of marker assisted selection or genomic selection in mink breeding programs for development of superior, highly efficient, and healthy animals. This approach will help improve the overall performance of the North American mink industry, which is now in difficulty due to several economic factors such as the high price of feed, declining price of fur and prevalence of diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".