Some Macromineral and Trace Mineral Levels in Milk of Different Dog Breeds
Bibliographic record
Abstract
Objective: In this study, it was planned to examine the mineral levels in the milk of different breed dogs during the lactation period. Calcium, magnesium, potassium, sodium, copper, zinc, manganese and iron concentrations were analyzed in dog milk throughout the lactation period. Materials and methods: In this research 6 Labradors, 6 German Shepherds, 6 Pointers, 5 Turkish Tazis, 5 Setters, 7 Malinois, and 5 Golden Retrievers (a total 40 dogs) of 3 to 4 years age were used as research materials, all of which were under same management and feeding conditions. All dogs were on diets appropriate for gestation and lactation periods. Adequate milk volume could be collected 2-3 weeks after parturition, and there were no known medical problems. Each day's samples were kept capped and refrigerated after being collected. The concentrations of calcium, magnesium, potassium, sodium, copper, zinc, manganese and iron, were analyzed by using Varian Brand 30/40 model AAS device. Results: The Ca, K, Na, Zn, Mn and Fe levels of milk samples from different dog breeds were found to have no significant difference . The highest Mg level was determined in Pointer breed milk samples, and the lowest was determined in Setter milk samples. The highest Cu levels amongst the inspected races were in Labrador milk samples, whereas the lowest levels were determined in Setter breeds. Conclusion:This data shows that most of the analyzed milk content of different breeds of dogs did not change significantly during the same lactation period, and any present difference could be taken into account when evaluating breeding studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".