Bibliographic record
Abstract
Stress is one of the most significant influences on behaviour and performance. The classical account is that stress mainly affects functions of the limbic system, such as learning, memory and emotion. Recent evidence, however, suggests that stress also modulates motor system function and influences the pathology of movement disorders. Most parts of the motor system show the presence of glucocorticoid receptors that render their circuits susceptible to the influence of stress hormones. Stress and glucocorticoids have been shown to modulate temporal and spatial aspects of motor performance. Skilled movements seem to be most prone to stress-induced disturbances, but locomotion and posture can also be affected. Stress can modulate movement through activation of the hypothalamic-pituitary-adrenal axis and via stress-associated emotional changes. The dopaminergic system seems to play a central role in mediating the effects of stress on motor function. This route might also account for the finding that stress influences the pathology of dopamine-related diseases of the motor system, such as Parkinson's disease. Clinical observations have indicated that stress might lead to the onset of Parkinsonian symptoms or accelerate their progression. Glucocorticoids are modulators of neuronal plasticity, thus determining the degree of structural and functional compensation of the damaged motor system. This may particularly affect slowly progressive neurodegenerative diseases, such as Parkinson's disease. That stress represents a significant modulator of motor system function in both the healthy and the damaged brain should be recognized when developing future therapies for neurodegenerative 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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".