Multiphasic nonlinear mixed growth models for laying hens
Bibliographic record
Abstract
Appropriate evaluation of BW and gain during rearing is required for optimal extended laying performance in laying hens. The objective of this study was to compare monophasic, diphasic, and triphasic Gompertz and logistic models describing BW and gain in individually fed free-run laying hens and to study the variation between individuals in shape parameters. Fifteen Lohmann Brown Lite hens were fed ad libitum from week 0 to 43 with a precision feeding system, measuring feed intake and BW individually in a group housed setting. Random variables related to mature weight and timing of maximum gain during the pubertal growth phase were introduced into the multiphasic model for BW with the best fit. For both the weight-age and gain-age functions, the diphasic and triphasic Gompertz and logistic model models fitted the data better than the monophasic models. The Gompertz model was able to identify the ages at the highest gain at similar time points for both BW and gain, whereas the logistic models failed to do so. The derivative of the multiphasic Gompertz models for the gain-age relationship identified age at the highest gain at similar ages as compared with the logistic models for gain. The mixed models predicted that the individual mature BW ranged from 1.83 kg to 2.10 kg and the variability in the timing of the highest rate of gain during the pubertal growth spurt ranged from 15.26 wk to 19.79 wk. Including random terms associated with the mature BW and the second inflection point of the diphasic Gompertz growth model allowed for identification of variability in the growth curve shape between individuals, which can be a tool to study the relationship between the individual growth curve shape and performance parameters.
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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) | 0.000 | 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".