Bibliographic record
Abstract
Rats, mice and other species can behaviorally anticipate a predictable daily mealtime by entrainment of circadian oscillators (food-entrainable oscillators) distinct from those (light-entrainable oscillators) that regulate light-dark entrained rhythms of behavior and physiology. Neurobiological analysis of food-anticipatory rhythms has progressed slowly but is gaining pace. Food-anticipatory rhythms have proven to be surprisingly robust to many neural and circadian clock gene perturbations. A few neural ablation sites or gene mutations have been associated with loss or marked attenuation of anticipatory rhythms, but in each case there are apparently conflicting reports. Attenuation of food-anticipatory rhythms following neural or genetic perturbations could result from actions upstream or downstream from the clock mechanism, and could be limited to certain behavioral endpoints or recording conditions. Failure to observe attenuation could reflect compensation by alternate timing mechanisms that do not involve food-entrainable oscillators. To facilitate progress in neurobiological analysis of food-anticipatory rhythms, criteria for distinguishing among formally distinct mechanisms by which animals might anticipate a daily meal are reviewed, and procedural variables that can affect the expression of food-anticipatory rhythms in neurobiologically intact or compromised animals are identified.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".