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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".