Application of the law of diminishing returns to partitioning metabolizable energy and crude protein intake between maintenance and growth in egg-type pullets
Bibliographic record
Abstract
Experiments designed to investigate the effect of dietary nutrient concentrations on the growth and development of pullets are relatively long term and expensive to conduct. As the cost of research increases, mathematical models become valuable tools to answer research and development questions. Modeling growth curves allows nutritionists and poultry researchers to predict dynamic or daily nutrient needs more adequately than using fixed requirements. The potential and validity of a specially reparameterized monomolecular model to partition nutrient intakes between requirements for maintenance and growth was previously demonstrated in relation to ruminants, pigs, chickens, turkeys, and broiler breeder pullets. In the current study, the model was evaluated for its ability to estimate ME and CP requirements for maintenance and growth in egg-type pullets. On the basis of the results of this study, along with those previously reported for chickens, turkeys, and broiler breeder pullets, this model is advantageous because it can predict the magnitude and direction of responses of growing poultry to dietary ME and CP intakes without requiring initial assumptions. The model also has the advantage of biological interpretability of the parameter estimates. One of the main consequences of this interpretability is that the results from several experiments can be pooled to obtain the best estimates of the response coefficients.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".