PSIV-3 Impact of meal size and feeding frequency on working dog health and performance.
Bibliographic record
Abstract
Abstract Working canines provide many services including emergency response, law enforcement, national security, and disability assistance. Due to varying environmental challenges, these canines require focus on feeding management for their gastrointestinal needs. Currently, there are little data available regarding the impact of smaller, more frequent meals on working canines. The objective of this study was to evaluate the effect of meal size and frequency on blood and fecal parameters in dogs. Variables of interest included fecal pH, frequency of fecal eliminations, and blood glucose. Labrador retrievers (n = 20), 1 yo, body condition score [BCS (4-5)], bred for canine detection work and receiving daily conditioning (exercise) were utilized in a 2x2 crossover design. All canines were acclimated to the basal diet for 21 d prior study initiation. Canines were randomly assigned to one of two treatment groups and were fed according to NRC guidelines to maintain BCS. Treatment group 1 received a single meal at the AM feeding (0700 h). Treatment group 2 received two meals with their total dietary intake divided equally across the am (0700) and pm (1500 h) feeding. First morning fecal eliminations were collected and analyzed for pH. All fecal eliminations (0600-1500) were collected and recorded for frequency of eliminations and sampled for pH values. Blood samples were collected via cephalic venipuncture at 0600 (baseline), 0900 (post AM), and 1700 (post PM) and used to measure blood glucose. Statistical analysis was conducted using SAS On Demand for Academics (P < 0.05). Fecal pH was unaffected by meal size (P = 0.3779; with 5.84 and 5.92 for once or twice daily meals, respectively. Similarly, fecal pH did not change throughout the day for either treatment (P = 0.3504; Single meal = 5.86, Twice daily meal = 5.97). Similarly, meal size did not affect frequency of fecal eliminations (P = 0.8897) with dogs in both groups eliminating 3 times during the day. There was a significant effect of period on blood glucose (P = 0.0096) where Period 1 (110.59mg/dL) was lower than Priod 2 (119.4 mg/dL; P = 0.0096), although both periods were still within normal range. Dogs in both treatment groups experienced lowered blood glucose levels throughout the day (P = 0.0092) when baseline (120.32 mg/dL) and post AM (116.22 mg/dL) values were compared to post PM (P =0.0073, 108.38 mg/dL). These results suggest both single and twice daily meal regimens can be successfully utilized without impact to fecal pH, elimination frequency, or blood glucose levels.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".