Overeating by Young Obesity‐prone and Lean Rats Caused by Tastes Associated With Low Energy Foods
Bibliographic record
Abstract
OBJECTIVE: Childhood obesity is a prominent health problem that may involve early learning about tastes and the energy content of foods. We tested the hypothesis that food tastes predictive of low energy content cause overeating in young animals. RESEARCH METHODS AND PROCEDURES: Juvenile and adolescent (4- and 8-week-old) male JCR:LA-cp lean (+/cp or +/+) and obesity-prone (cp/cp) rats were given sweet (saccharin) and salty (sodium chloride) gelatin cubes made with starch (high caloric) or no starch (low caloric) for 16 days of taste conditioning. After 10 hours of food deprivation, rats received pre-meals with flavors that had been paired or unpaired with high caloric content during taste conditioning, followed immediately by measurement of chow intake at regular meals. RESULTS: Our findings show that both lean (+/cp) and obesity-prone (cp/cp) juvenile rats ate more regular chow after a pre-meal with a flavor associated with low caloric value than after a similar pre-meal with a flavor predictive of high caloric content. This effect occurred with juvenile rats but not with adolescents. DISCUSSION: Data from our study indicate that the subversion of the relationship between taste and caloric content disrupts the normal physiological and behavioral energy balance of juvenile rats, resulting in overeating that is independent of genetic disposition for obesity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".