Cost Optimization of Homemade Diet for Dogs
Bibliographic record
Abstract
Nowadays, people raising pet animals in Turkey is increasing daily. The feeding of dogs, which are members of the houses as valuable assets, is at least as necessary as family members. Calculation of dogs' daily nutrient requirements, maintenance, growth, pregnancy, lactating, working, etc. are very variable and require an intense estimate. Feeding pet dogs only with industrially prepared foods can affect the economy of the family and the health of dogs negatively. Mainly, it is continuously questioned by the animal owners whether foods and additives that may harm health are used in industrially prepared foods. Desktop, web, and mobile-based software are used in the animal feeding area. Nevertheless, according to the researches, there is no web-based software that is used for dog diet preparation that can be used by dog owners who can calculate precisely the daily nutrient requirements of dogs and meet these requirements with available ingredients so far. The data used in this study were taken from Selcuk University, Faculty of Veterinary Medicine, Animal Science and Animal Nutrition Department. In this study, a linear programming model is proposed to calculate dog diets that both can meet the nutrient requirements of dogs and can engage cost optimization. User-friendly web-based dog diet preparation software is performed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".