Multiphasic mixed growth models for turkeys
Bibliographic record
Abstract
Growth models are important for optimization of feed formulation and breeding programs in turkeys. The objectives of this study were 1) to compare sex and line differences in turkeys in parameter estimates of mono- and di-phasic Gompertz growth models, and 2) to evaluate mono and diphasic mixed Gompertz growth models to determine the variation in parameter estimates in a group of female line turkey toms. A total of 1,056 manually recorded weekly average body weight (BW) observations from male and female turkeys of a male and female line from weeks 1 to 20 were used for objective 1. Daily median values of automatically collected individual BW of female line turkey toms were used for objective 2 and random components associated with individual subject animals related to mature weight and/or timing of maximum gain during each phase were introduced in the Gompertz model. Growth curve shapes were different between male line toms, male line hens, female line toms, and female line hens (P < 0.001). However, inflection points were similar between male and female line toms and between male and female line hens (14.06 vs. 13.72 wk and 11.22 and 10.71 wk, respectively), while mature BW differed between lines by 6.49 and 3.81 kg for toms and hens, respectively. The normalized growth rate constant (growth rate constant corrected for mature weight) was around the same magnitude between male and female line toms (0.0031 vs. 0.0038, respectively), but slightly lower in male line hens compared to female line hens (0.0072 vs. 0.0091, respectively). Diphasic Gompertz models described growth better in all line × sex combinations compared to the monophasic models (P < 0.001) and mixed diphasic Gompertz models showed improved fit over mixed monophasic Gompertz models. The correlation structure of the random components identified that individuals with a higher mature weight had a later inflection point and lower growth rate coefficients. These results provide tools for improved breeding practices and a structure to evaluate the effects of dietary or environmental factors on growth trajectories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".