Bibliographic record
Abstract
Modeling of broiler chicken supply-chain economics depends on robust biological models of growth and yield of broiler chickens. In this paper, 8 dynamic nonlinear broiler carcass and carcass part yield models were evaluated statistically for their suitability for predicting weights of carcass parts. The analysis employed 4 sigmoidal (S) models (Gompertz, modified Gompertz, Richards, and Lopez) describing carcass part weight as a function of age, as well as 3 diminishing returns (DR) models (Lopez, Mitscherlich, and log linear), and a log-linear proportional yield (PY) model, which describe carcass part yield and weight, respectively, as a proportion of feather- and fat-free empty body mass (FFEBM). Three S models with a flexible point of inflection were better able to predict carcass part weights than a fixed point of inflection Gompertz model and, in general, the DR models. The log-linear models were the only models that converged in 100% of the evaluations. The allometric PY model predicted weights for most carcass parts with the smallest degree of error and with substantially less bias than the DR log-linear model. Estimates of the coefficients for the log-linear PY model are included for 12 key carcass parts. Estimates of carcass chemical composition are presented for the log-linear PY model.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".