12 Genetic and phenotypic parameters for Aleutian disease tests and their correlations with growth and pelt quality traits in American mink
Bibliographic record
Abstract
Abstract Aleutian disease brings tremendous financial losses to the mink industry. The ineffective immunoprophylaxis, medication, and culling strategies have urged the mink industry to select mink with low quantitative enzyme-linked immunosorbent assay (qELISA) score or negative counterimmunoelectrophoresis (CIEP) test result. However, little is known about the heritabilities of qELISA and CEIP as well as their relationships with growth and pelt quality traits. The traits, including qELISA, CIEP, body length at harvest (HLEN), the size of dried pelt (SIZE), the overall quality of dried pelt (QUA), and the nap length of dried pelt (NAP), were measured on 1,683 American mink from the Canadian Center for Fur Animal Research (Nova Scotia, Canada) and Millbank Fur Farm (Ontario, Canada). Significance (P < 0.05) of fixed effects (sex, farm, age, and color) and random effects (common litter, permanent environment, and dam) were determined by univariate analyses, while genetic and phenotypic parameters for all traits were estimated under bivariate analyses using ASREML 4.1. Estimated heritabilities (±SE) were 0.41±0.07 for qELISA, 0.06±0.06 for CIEP, 0.39±0.06 for HLEN, 0.46±0.07 for SIZE, 0.25±0.06 for QUA, and 0.46±0.08 for NAP. The qELISA showed non-significant (P > 0.05) genetic correlations with HLEN (0.05±0.13) and dried pelt traits (0.02±0.18 with SIZE, -0.21±0.20 with QUA, and -0.13±0.16 with NAP). The CIEP only showed a significant (P < 0.05) negative genetic correlation with SIZE (-0.85±0.33). The moderate-to-high heritabilities of qELISA, HLEN, SIZE, QUA, and NAP indicated that these traits can be genetically improved through a genetic/genomic selection. The low and non-significant heritability of CIEP indicated the ineffectiveness of direct selection for this trait. The estimated genetic parameters for qELISA suggested that selection for lower qELISA scores may not interfere with the selection of pelt size and quality in the genetic improvement programs of American mink.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".