A sinusoidal equation as alternative to conventional growth functions to describe the evolution of growth in quail
Bibliographic record
Abstract
Aim of study: The aim of the present study was to introduce a sinusoidal equation into poultry science by applying it to temporal growth data from quail.Material and methods: To examine the performance of the sinusoidal equation in describing the growth patterns of quail, four conventional growth functions (Gompertz, logistic, López and Richards) were used as reference in this study. Comparison of models was carried out by analysing model behaviour when fitting the curves using nonlinear regression and assessing statistical performance. Maximum log-likelihood estimation, mean squared error, Akaike and Bayesian information criteria were used to evaluate the general goodness-of-fit of each model to the different data profiles.Main results: The selected sinusoidal equation precisely describes the growth dynamics of quail. Comparison of the growth functions in terms of the goodness-of-fit criteria revealed that the sinusoidal equation was one of the most appropriate functions to describe the age-related changes of bodyweight in quail.Research highlights: To the best of our knowledge there are no studies available on the use of sinusoidal equations to describe the evolution of growth in quail. The sinusoidal equation used in this study represents a suitable alternative to conventional growth functions to describe the growth curves for a range of strains/lines of male and female Japanese quail.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".