48 Genomic Studies of Feed Efficiency and Component Traits in American Mink
Bibliographic record
Abstract
Abstract Feed costs are the largest expense in mink production systems. Therefore, improvement of feed efficiency (FE) is the best way to use limited resources efficiently and increase the mink industry’s sustainability. The objectives of this project are to 1) identify the genetic relationships among different FE measures and their component traits, and 2) discover the genetic architecture of FE and implement genomic selection for FE traits to increase the genetic gain in American mink. Final body weight (FBW), final body length (FBL), daily feed intake (DFI), average daily gain (ADG), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG) and Kleiber ratio (KR) traits were measured based on the phenotypic records on 1,088 American mink from the Canadian Center for Fur Animal Research (Nova Scotia, Canada). Univariate models were applied to test the significance of sex, color type, age, and nested Row(Year) as fixed effects and random maternal effect. Genetic parameters were estimated via bivariate models using ASReml-R 4. Estimated heritabilities (±SE) were 0.38±0.10, 0.36±0.10, 0.25±0.10, 0.34±0.09, 0.38±0.08, 0.37±0.07, 0.29±0.10, 0.32±0.10 and 0.34±0.10 for FBW, FBL, DFI, ADG, FCR, RFI, RG, RIG and KR, respectively. RFI showed non-significant (P >0.05) genetic correlations with component traits such as FBW (0.00±0.17) and FBL (0.30±0.16) but significant (P < 0.05) high genetic correlation with DFI (0.74±0.09), indicating that selection based on RFI will reduce the feed intake without any negative effects on the size and growth. The estimated genetic parameters for FE traits suggested the possibility to implement genetic/genomic selection to improve the FE in American mink. Consequently, the ongoing project on genetic mapping and genomic selection will enhance the knowledge of FE and improve the efficacy of selection for more feed-efficient 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 | 0.000 | 0.000 |
| 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".