54 Genetics Reloaded: Large-scale Collection of Novel Phenotypes in Turkey
Bibliographic record
Abstract
Abstract Effective application of genomic selection methodologies require genomic information, population-based pedigree data, and high-quality phenotypes. The implementation of such selection programs can increase the accuracy of breeding values, therefore improving the ability to estimate the genetic merit of livestock. In particular, traits with low heritability are amenable to genomic selection. The objective of this presentation is to outline improvement of traits such as livability, disease resistance, fertility, and other health and welfare traits in turkeys, which could considerably advance breeding programs. The aim of this study was to apply different methodologies (ssGBLUP, random regression approaches, etc.) to novel and conventional traits collected in commercial turkey lines (15,000 turkeys genotyped at 65K). This reference population was used to assess the increase in accuracy of selection using genomic information, which ranged from 0 to +0.3, depending on the trait. A further goal of the study was to monitor inbreeding within the different lines. A large number of high-quality phenotypes related to fertility, growth, production, and carcass composition were collected, as well as additional health and behaviour phenotypes related to livability. These traits are being developed for use in performance testing. Furthermore, meat quality (e.g. white striation, water holding capacity, pH, sheer force and colour, etc.) and total carcass composition phenotypes were analysed. With improved methodology, more detailed phenotypic information, and comprehensive data collection and integration, we present more accurate selection of parent stock for application in applied poultry breeding programs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".