Narushin-Takma models as flexible alternatives for describing economic traits in broiler breeder flocks
Bibliographic record
Abstract
Three Narushin-Takma (NT) models (NT1, NT2, and NT3) were examined for their ability to describe different curves obtained from broiler breeder flocks. The models NT1, NT2, and NT3 comprise 3 flexible mathematical functions (rational polynomial functions) with 5, 6, and 7 parameters, respectively. The characteristics fitted were BW, egg production, egg mass, egg weight, first- and second-grade eggs, hatchability, feed intake, and feed conversion ratio. To evaluate the ability of these NT models to fit the different curves, comparisons were made with more commonly fitted functions (Gompertz, modified compartmental, Richards, Adams-Bell, and Lokhorst). Comparisons revealed a higher accuracy of fit with the NT models, proving their general flexibility. This study likely represents the first time a generic model has been demonstrated to fit all these characteristics satisfactorily. Results showed that in most cases, NT3, because of its greater number of parameters, gave the highest accuracy of prediction. The NT models are likely to fit most curves and are therefore advocated for accurate prediction of other traits with a minimum of mathematical complexity.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".