120 Seasonal Variation of Select Serum Electrolyte Concentrations in Siberian Huskies Housed Outside in Ontario Canada from may Until October
Bibliographic record
Abstract
Abstract The effect of season has been recognized in both human and veterinary medicine for its influence on various hematological and biochemical parameters. However, limited research has been conducted in dogs regarding seasonal electrolyte changes, especially in those non-exercising and housed outdoors. Therefore, the objective of this study was to evaluate the effects of seasonal variation in ambient conditions on electrolyte status in 28 outdoor-housed adult Siberian huskies (13 females: 8 spayed, 5 intact and 15 males: 9 neutered, 6 intact). Dogs had an average age of 5.3 ± 2.8 years and body weight of 23.22 ± 3.73 kg. Dogs were fed one of four dry extruded diets formulated with the same mineral premix. Fasted blood samples were collected monthly from May until October and sera were analyzed for Cl-, Na+, K+, Ca2+, Mg2+ and P using photometric analysis. Data were analyzed using PROC GLIMMIX of SAS with dog as a random effect and temperature, spay-neuter status, and age as fixed effects. Serum Na+, K+, Ca2+, and P concentration were least in May, and greatest in September, October, October, and June, respectively (P< 0.05). Conversely, serum Mg2+ were greatest in May and September, and least in August (P< 0.05). Serum Cl- were greatest in May and October, and least in July (P< 0.05). Across all months, females had decreased serum Ca2+ than males, intact dogs had greater serum Cl-, and younger dogs (<3 years of age) had greater serum P compared with older dogs (P< 0.05). These results suggest that dogs experience significant changes in electrolyte balance due to seasonal and ambient variation. Those that are higher risk for electrolyte loss and imbalance may benefit from supplementation during the summertime. Further research is needed to understand if seasonal or ambient variations drive these changes and if they compromise athletic performance.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".