Bibliographic record
Abstract
Growth and development are complex phenomena. To date, most growth modeling research has focused on a single growth phase, which is sufficient and useful for describing ad libitum fed animals processed at a prepubertal age, such as broilers or turkeys produced for meat. However, multiphase growth models are necessary to describe and predict growth and further to hypothesize about optimizing growth of reproducing animals such as broiler breeder hens. Therefore, the objective of the present study was to develop and evaluate multiphasic models to describe the growth of various types of poultry raised to reproductive age. Coefficients for monophasic, diphasic, and triphasic Gompertz model forms were estimated using a variety of BW trajectories published by primary breeders. The fit of these models was evaluated for a representative laying line hen, broiler breeder hen and rooster, and turkey hen. The coefficient of determination (R2), root mean square error, and the Bayesian information criterion were used to evaluate the fit of each model. The diphasic model was found to be the best fit for the turkey hen, while the triphasic model was the most suitable model for all the chicken lines studied. Hypotheses can be formulated based on any of the continuous model parameters, and the resulting BW trajectories can be implemented and evaluated in a systematic way. The biological relevance of the continuous parameters in multiphasic Gompertz models provides an opportunity to implement a robust hypothesis-based approach for future optimization of growth curves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".