Aspects of selection for feed efficiency in meat producing poultry
Bibliographic record
Abstract
Over the last five years, the costs of poultry feed ingredients have increased substantially. This has been due to an increased use of corn for ethanol production and a greater overall global feed grain demand. Across the poultry industry this has led to higher production costs and reaffirmed the importance of feed efficiency on profitability. The effect that an increase in feed costs has on profitability is a clear driver for the selection for birds with better feed efficiency. Feed efficiency selection can be achieved using a number of different analytical methods. Selection for feed conversion ratio (FCR) has been used to improve feed efficiency with success but using a ‘ratio’ trait has mathematical limitations because selection pressure tends to be placed on the component traits of FCR in a non-linear manner. Another measure, residual feed intake (RFI) shows moderate to high heritability and does not have the mathematical limitations associated with FCR. RFI has little to no correlation with production traits and this indicates that genetic improvement of RFI within a selection index can be done without the confounding issues inherent with FCR. Improvements in RFI or FCR have a favourable effect on environmental emissions and decreases the environmental impact of poultry production. The current global production of ammonia, CH4, and N2O by the poultry industry is significant, at levels of 2.1, 29.44 and 279 million tonnes CO2eq, respectively. Reductions in emissions can be achieved via improvements in feed efficiency by lowering amounts of manure excreted and decreasing emitted by-products such as ammonia and greenhouse gases (N2O, CO2 and CH4). Consequently, improvements in feed efficiency can not only increase profitability of the poultry industries by lowering production costs but also decrease environmental impact by reducing environmental emissions.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 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".