Bibliographic record
Abstract
Caffeine can improve exercise performance when it is ingested at moderate doses (3-6 mg/kg body mass). Caffeine also has an effect on the central nervous system (CNS), and it is now recognized that most of the performance-enhancing effect of caffeine is accomplished through the antagonism of the adenosine receptors, influencing the dopaminergic and other neurotransmitter systems. Adenosine and dopamine interact in the brain, and this might be one mechanism to explain how the important components of motivation (i.e. vigor, persistence and work output) and higher-order brain processes are involved in motor control. Caffeine maintains a higher dopamine concentration especially in those brain areas linked with 'attention'. Through this neurochemical interaction, caffeine improves sustained attention, vigilance, and reduces symptoms of fatigue. Other aspects that are localized in the CNS are a reduction in skeletal muscle pain and force sensation, leading to a reduction in perception of effort during exercise and therefore influencing the motivational factors to sustain effort during exercise. Because not all CNS aspects have been examined in detail, one should consider that a placebo effect may also be present. Overall, it appears that the performance-enhancing effects of caffeine reside in the brain, although more research is necessary to reveal the exact mechanisms through which the CNS effect is established.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".