Determination of the optimum slaughter weight to maximize gross profit in a turkey production system
Bibliographic record
Abstract
A deterministic model was used to optimize turkey slaughter weight based on a profit equation that described the commercial grower and processing divisions of an integrated company. The objective was to determine optimum slaughter weights for toms and hens using both a heavy and super heavy strain, to maximize gross margin of the system. Sensitivity of optimum slaughter weight in response to feed cost and breast meat price was also considered. Higher margins could be achieved with toms and super heavy strain birds. This indicated that larger birds, from a heavier weight strain or toms within a strain, could be more efficient and profitable given the assumed production values. Results were based on the assumed market conditions, and changes in the costs or turkey component values (i.e., breast meat) could result in a shift in the optimal turkey strain to use. Increased feed cost results in a lower optimum slaughter weight and decreased margin. Optimum slaughter weight and profit increased with higher breast meat values. Increasing the profit of an integrated company can be accomplished by targeting slaughter to an optimum weight.Key words: Turkey, body weight, production efficiency, breast meat, feed efficiency
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".