A comparative evaluation of functions for the analysis of growth in turkeys.
Bibliographic record
Abstract
Animal Sciences Group, Faculty of Agriculture, University of Ilam , Ilam 69315/516, Iran, Centre for Nutrition Modelling, Department of Animal and Poultry Science, University of Guelph, Guelph ON, N1G 2W1, Canada, Departamento de Produccion Animal, Universidad de Leon, E-24007 Leon, Spain Corresponding author: Darmani_22000@yahoo.com __________________________________________________________________________________ Three mathematical functions, used previously to describe the relationship between body weight (BW) gain and metabolizable energy (ME) intake in broilers, were used in this survey with growing turkeys to investigate the relationships between BW and cumulative ME intake (cMEI), and between BW gain and crude protein (CP) intake in two different studies. All statistical analyses were performed using the mixed non-linear procedure of SAS (SAS 2000). In the first analysis, two functions (monomolecular and Richards equations) were assessed as candidates for describing the relationship between BW and cMEI. When the Richards equation was fitted, the additional parameter n tended to the value -1, resulting in the monomolecular equation as a special case of this generalised function. Therefore, it was concluded that the monomolecular equation was adequate to describe the relationship between BW and cMEI. In the second analysis, the scope of a specifically re-parameterized monomolecular equation was extended to growing turkeys to provide an estimate of their CP requirements for maintenance and growth. The estimated maintenance requirement (3.95 g/kg of BW/d) and the calculated values of efficiency of utilization of protein for growth (0.64) were in good agreement with values reported previously by other researchers.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".