Multiple entrained oscillator model of food anticipatory circadian rhythms
Bibliographic record
Abstract
For many animal species, knowing when to look for food may be as important as knowing where to look. Rats and other species use a feeding-responsive circadian timing mechanism to anticipate, behaviorally and physiologically, a predictable daily feeding opportunity. How this mechanism for anticipating a daily meal accommodates more than one predictable mealtime is unclear. Rats were trained to press a lever for food, and then limited to one or more daily meals at fixed or systematically varying times of day. The rats were able to anticipate up to 4 of 4 daily meals at fixed times of day and two 'daily' meals recurring at 24 h and 26 h intervals. When deprived of food, in constant dark, lever pressing recurred for multiple cycles at expected mealtimes, consistent with the periodicity of the prior feeding schedule. Anticipation did not require the suprachiasmatic nucleus circadian pacemaker. The anticipation rhythms could be simulated using a Kuramoto model in which clusters of coupled oscillators entrain to specific mealtimes based on initial phase and intrinsic circadian periodicity. A flexibly coupled system of food-entrainable circadian oscillators endows rats with adaptive plasticity in daily programming of foraging activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".