Daytime increase in caloric intake without change in total 24-h caloric intake can increase adiposity but not total bodyweight in rats with inverted feeding pattern
Bibliographic record
Abstract
The goal of this study was to evaluate the effect of the food availability period on body weight, self-selection of macronutrients, adiposity, lipoprotein, and serum glucose profiles without changing energy intake. Young male rats were divided into 2 groups according to the availability of food during the light and dark phases of the cycle, forming 2 groups: control group (CG) and group with inverted feeding pattern (IFPG). Before inversion of food availability on the 80th day, circadian food intake was measured every 4 h over 24 h during 3 days. The glycemic curve, an oral test for glucose tolerance, and self-selection of macronutrients were evaluated. Blood samples were collected for analysis of fasting glucose, triglycerides, and total cholesterol fractions. The IFPG showed an increase in fasting glucose in the dark phase of the cycle, changes in the glycemic curve, and oral glucose tolerance test. It also showed increased abdominal and liver fat and distinct choice of macronutrients compared with the CG. A change in the availability of food according to the phase of the circadian cycle produces changes in glucose and feeding circadian rhythm culminating in increased abdominal and hepatic fat. These effects can increase the risk of metabolic disorders and installation of chronic diseases.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".