Bibliographic record
Abstract
Estrogen plays an important role in regulating energy and fat metabolism, and maintaining normal body composition in both female and male animals. Both soy isoflavones and exogenous estrogen have been shown to significantly reduce body fat accumulation in ovariectomized rodents, indicating soy isoflavones may act as estrogen agonist in regulating energy and fat metabolism, and body composition. Obesity has been associated with many chronic diseases in dogs, and spaying/neutering is among the risk factors for overweight and obesity in dogs. There is no literature concerning the effects of soy isoflavones on weight gain in spayed/neutered dogs. In this study, we fed spayed/neutered Labrador Retrievers with either a control diet (Ctl, n=13) or a test diet containing soy isoflavones (Iso, n=14) that came from soy germ meal. Both diets had similar amounts of dietary protein, fat and caloric density. The dogs were fed 25% more than their maintenance energy requirement. At the end of the 12‐month study, the dogs in the Ctl group gained twice as much body weight as the dogs in the Iso group (5.54 vs. 2.68 kg, p < 0.05). In addition, soy isoflavones‐containing diet did not significantly affect total white blood cells, thyroid hormone profile, and many other blood chemical parameters. For the first time, the results from our study showed that soy isoflavones‐containing diet was very effective in reducing body fat accumulation in spayed/neutered dogs.
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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) | 0.001 | 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 | 0.000 |
| 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 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".