Evaluating the egg production of broiler breeder hens in response to dietary nutrient intake from 31 to 60 weeks of age through neural network models
Bibliographic record
Abstract
Faridi, A., Golian, A. and France, J. 2012. Evaluating the egg production of broiler breeder hens in response to dietary nutrient intake from 31 to 60 weeks of age through neural network models. Can. J. Anim. Sci. 92: 473–481. The aim of this study was to evaluate the response of broiler breeder hens in terms of egg production to dietary nutrient intake. Using neural network (NN) models and breaking down the collected data from 98 commercial broiler breeder houses into 3-wk intervals, 10 NN-based models were developed from 31 to 60 wk of age. The data lines were divided into two random subsets of training (n=64) and testing (n=34) sets. The variables of interest for developing the models were metabolizable energy (ME; kcal bird−1 d−1), and crude protein (CP), total sulphur amino acids (TSAA), lysine (Lys), calcium (Ca) and available phosphorus (AP), all in g bird−1 d−1. The random optimization algorithm was applied to the constructed models to find the optimal level of the input variables which maximized egg production during the different intervals. The high R 2 values in all the developed models for both the training and testing sets indicate the accuracy of NN-based models in estimating egg production. The optimization results revealed that breeder hens consuming 485, 473, 471, 466, 460, 452, 448, 442, 437 and 445 kcal of ME bird−1 d−1 showed the highest egg production during the 10 consecutive 3-wk intervals from 31 to 60 wk of age, respectively. Moreover, the optimal performance of hens required the following average intakes from 31 to 60 wk of age (g bird−1 d−1): CP: 23.7; TSAA: 1.05; Lys: 1.07; Ca: 4.91; and AP: 0.58. The results show that energy (kcal bird−1 d−1) and other nutrient requirements (g bird−1 d−1) of broiler breeder hens from 31 to 60 wk of age do not change in consort together with age; therefore using different diets with different dietary nutrient levels during the production cycle may help the nutritionists better meet the requirements of broiler breeder hens. Based on the present study, it appears that company guideline recommendations may underestimate the nutrient requirements of hens during these weeks when egg production is declining gradually.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".