Are Owls and Larks Different When it Comes to Aggression? Genetics, Neurobiology, and Behavior
Bibliographic record
Abstract
This review focuses on the contribution of circadian rhythms to aggression with a multifaceted approach incorporating genetics, neural networks, and behavior. We explore the hypothesis that chronic circadian misalignment is contributing to increased aggression. Genes involved in both circadian rhythms and aggression are discussed as a possible mechanism for increased aggression that might be elicited by circadian misalignment. We then discuss the neural networks underlying aggression and how dysregulation in the interaction of these networks evoked by circadian rhythm misalignment could contribute to aggression. The last section of this review will present recent human correlational data demonstrating the association between chronotype and/or circadian misalignment with aggression. With circadian rhythms and aggression being a burgeoning area of study, we hope that this review initiates more interest in this promising and topical area.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".