PSVII-17 Effects of steaming or soaking hay on acute glycemic response in Standardbred racehorses.
Bibliographic record
Abstract
Soaking or steaming hay are processes which are both known to alter hay nutrient content, including non-structural carbohydrates (NSC). Since high NSC content in hay has been identified as a risk factor for development of some insulin-related disorders in horses, this study sought to examine acute glycemic response in horses after being fed dry hay, hay soaked overnight, or hay steamed for 1 hour and left to cool overnight. In a 3 x 3 Latin square design, blood glucose was measured every 30 min from nine Standardbred racehorses (mean ± s.d. bwt, 472 ± 41.3 kg; 4 geldings, 1 colt, 4 mares; 1.5 – 9 years of age) for six hours following a meal of 0.5% BW of treatment hay. Nutrient analyses revealed that soaked, but not steamed hay, had significantly lower concentrations of NSC, water-soluble carbohydrates (WSC), and ethanol soluble carbohydrates (ESC) in contrast to the same dry hay (P<0.0001). Peak glucose was higher in horses fed dry hay compared to those fed steamed (P=0.0617), however, soaked did not differ from either dry or steamed hays. This indicates that steaming hay does have an influence on acute glycemic response in horses, however the cause of this effect is unknown as steaming hay did not decrease NSC, WSC, or ESC. Future research should investigate how insulinemic response is affected by providing steamed or soaked hay, and how both insulin and glucose are affected by long term ingestion of these processed hays.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".