Comparison of urine composition of healthy Labrador Retrievers and Miniature Schnauzers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To compare urine composition in Labrador Retrievers (LR) and Miniature Schnauzers (MS) fed the same dog food. ANIMALS: 8 healthy LR (mean [+/- SD] age, 3.1+/-1.7 years) and 8 healthy MS (mean age, 3.7+/-1.3 years). PROCEDURE: A nutritionally complete dry dog food was fed to the dogs for 24 days. Urinary pH, volume, specific gravity, frequency of urination, and urinary concentrations of 12 analytes were measured for each dog; urinary relative supersaturation (RSS) with calcium oxalate and brushite (calcium hydrogen phosphate dihydrate) were calculated from these values. RESULTS: MS urinated significantly less often and had a lower urine volume (ml/kg of body weight per d) and a significantly higher urine pH, compared with LR. Urinary calcium concentration and brushite RSS were significantly higher in the urine of MS. As a result of a high calorie requirement, primarily as a result of high surface area to volume ratio, MS had significantly higher intake (per kg body weight) of dietary minerals, compared with LR. CONCLUSIONS AND CLINICAL RELEVANCE: Differences in urine composition exist between breeds fed the same diet, some of which, including lower urine volume, higher calcium concentration, and higher brushite RSS, may contribute to the high prevalence of calcium oxalate uroliths observed in MS. Differences between breeds should be considered when evaluating strategies for controlling calcium oxalate stone formation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it