Calculating economic values for turkeys using a deterministic production model
Bibliographic record
Abstract
Modern poultry breeding has been successful in achieving significant gains in production due to high fecundity, relatively short generation interval (in comparison with other species) and, last, the application of scientific processes in genetic evaluation. The objective of this paper was to document the development of an economic model relevant to the integrated turkey industry and to use the model to describe appropriate breeding objectives by calculating economic values for important production traits. The industry was modelled from the multiplier breeder down through to the processor. Each level in the production chain used a unit of production such as a live poult produced, a carcass delivered at the processing plant or a processed unit of meat to scale between different production divisions. Growth rate, feed consumption and breast meat yield all had similar relative economic value, while the reproductive traits (egg production, fertility and hatchability) had similar economic values to each other, but were smaller in comparison with the commercial production traits. The model was sensitive to assumed costs, such as feed price and, also for assumed returns in the form of breast meat value, and, as a consequence, care must be taken in the assumed pricing structure when calculating economic values for turkey breeding.Key words: Economic model, economic value, turkeys, breeding objectives
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".