Genetic and phenotypic parameters for feed efficiency and component traits in American mink
Bibliographic record
Abstract
Feed cost is the largest expense of mink production systems, and, therefore, improvement of feed efficiency (FE) through selection for high feed-efficient mink is a practical way to increase the mink industry's sustainability. In this study, we estimated the heritability, phenotypic, and genetic correlations for different FE measures and component traits, including harvest weight (HW), harvest length (HL), final body length (FBL), final body weight (FBW), average daily gain (ADG), daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG), and Kleiber ratio (KR), using data from 2,288 American mink (for HW and HL), and 1,038 to 1,906 American mink (for other traits). Significance (P < 0.05) of fixed effects (farm, sex, and color type), a covariate (age of animal), and random effects (additive genetic, maternal, and common litter) were evaluated through univariate models implemented in ASReml-R version 4. Genetic parameters were estimated via fitting a set of bivariate models using ASReml-R version 4. Estimates of heritabilities (±SE) were 0.28 ± 0.06, 0.23 ± 0.06, 0.28 ± 0.10, 0.27 ± 0.11, 0.25 ± 0.09, 0.26 ± 0.09, 0.20 ± 0.09, 0.23 ± 0.09, 0.21 ± 0.10, 0.25 ± 0.10, and 0.26 ± 0.10 for HW, HL, FBL, FBW, ADG, DFI, FCR, RFI, RG, RIG, and KR, respectively. RIG had favorable genetic correlations with DFI (-0.62 ± 0.24) and ADG (0.58 ± 0.21), and nonsignificant (P > 0.05) genetic correlations with FBW (0.14 ± 0.31) and FBL (-0.15 ± 0.31). These results revealed that RIG might be a superior trait as it guarantees reduced feed intake with faster-growing mink yet with no negative impacts on body weight and length. In addition, the strong positive genetic correlations (±SE) between KR with component traits (0.88 ± 0.11 with FBW, 0.68 ± 0.17 with FBL, and 0.97 ± 0.02 with ADG) suggested KR as an applicable indirect measure of FE for improvement of component traits as it did not require the individual feed intake to be measured. Overall, our results confirmed the possibility of including FE traits in mink breeding programs to effectively select feed-efficient animals.
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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 | 0.001 | 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) | 0.001 | 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".