Bibliographic record
Abstract
Obesity and related metabolic health issues are a growing human threat, with many theories regarding its causes. In swine, physiologically alike to humans, considerable knowledge on obesity mechanisms has been accumulated. Calorie counting is the basis for managing swine diets and applied with great accuracy. Epigenetic programing predisposes pigs to insulin insensitivity, but pigs seem to sense this insensitivity and consequently eat less, preventing obesity. Pigs naturally prefer to eat small breakfasts and large dinners. Deviating from this eating pattern or providing diets with a high glycemic burden can trigger obesity; however, pigs will restrict food intake to prevent serious obesity. Interestingly, in practice, problems with obesity are rarely seen, even when pigs are fed poorly timed diets similar to junk food, likely because swine diets are balanced for every nutrient. Indeed, feeding pigs diets deficient in micronutrients does trigger obesity. For humans, several micronutrient requirements have not been set officially, and diets optimized for all micronutrients are rarely provided. In conclusion, various obesity triggers are being debated for humans, which have been proven in swine. Obesity problems in pigs are nevertheless less excessive, likely because pigs recognize unhealthy eating practices and consequently reduce food intake to avoid serious complications. Finally, swine diets are normally balanced for all nutrients, which may be an important practice to prevent obesity, from which human health could greatly benefit.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
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".