PSVIII-38 Late-Breaking Abstract: Estimating genetic parameters of feed efficiency traits in American mink
Bibliographic record
Abstract
Abstract Feed cost is the major input cost in the mink industry and thus improvement of feed efficiency through selection for high feed efficient mink is necessary for the mink farmers. The objective of this study was to estimate the heritability, phenotypic and genetic correlations for different feed efficiency measures, including final body weight (FBW), daily feed intake (DFI), average daily gain (ADG), feed conversion ratio (FCR) and residual feed intake (RFI). For this purpose, 1,088 American mink from the Canadian Center for Fur Animal Research at Dalhousie Faculty of Agriculture were recorded for daily feed intake and body weight from August 1 to November 14 in 2018 and 2019. The univariate models were used to test the significance of sex, birth year and color as fixed effects, and dam as a random effect. Genetic parameters were estimated via bivariate models using ASReml-R version 4. Estimates of heritabilities (±SE) were 0.41±0.10, 0.37±0.11, 0.33±0.14, 0.24±0.09 and 0.22±0.09 for FBW, DFI, ADG, FCR and RFI, respectively. The genetic correlation (±SE) was moderate to high between FCR and RFI (0.68±0.15) and between FCR and ADG (-0.86±0.06). In addition, RFI had low non-significant (P > 0.05) genetic correlations with ADG (0.04 ± 0.26) and BW (0.16 ± 0.24) but significant (P < 0.05) high genetic correlation with DFI (0.74 ± 0.11) indicating that selection for lower RFI will reduce feed intake without adverse effects on the animal size and growth rate. The results suggested that RFI can be implemented in genetic/genomic selection programs to reduce feed intake in the mink production system.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".