Evaluation of non‐linear growth curve models in the Vietnamese indigenous Mia chicken
Bibliographic record
Abstract
Understanding of animal growth is important for the improvement of management and feeding practices; however, little is known about the growth curve in Vietnamese indigenous chicken. This study was performed to determine the most appropriate models for describing the growth curve of Vietnamese Mia chicken. The study evaluated the performances of the Logistic, Gompertz, Richards, and Bridges models of body weights in 224 Mia chickens. Models were fitted using minpack.lm package in R software and Akaike's information criterion and Bayesian information criterion were used for model comparison. Based on these criteria, the Gompertz and Bridges were the best models for males and females, respectively. Estimated asymmetric weights (α) were ranged from 2,241.91 ± 14.74 (g) (Logistic) to 2,623.86 ± 30.23 (g) (Gompertz) for males and from 1,537.36 ± 10.97 (g) (Logistic) and 1,958.36 ± 72.92 (g) (Bridges) for females, respectively. The age at the inflection point was estimated from 9.32 to 10.5 weeks and from 8.51 to 9.86 weeks for males and females, respectively. In conclusion, the Gompertz model is the most suitable model for describing the growth curve of Mia chicken. The parameters obtained from growth models could help define feeding programs to meet nutritional needs from hatching to the age of maximum growth, reproduction programs, and marketing strategies.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".