Bibliographic record
Abstract
Caffeine is a popular work-enhancing supplement that has been actively researched since the 1970s. The majority of research has examined the effects of moderate to high caffeine doses (5-13 mg/kg body mass) on exercise and sport. These caffeine doses have profound effects on the responses to exercise at the whole-body level and are associated with variable results and some undesirable side effects. Low doses of caffeine (<3 mg/kg body mass, ~200 mg) are also ergogenic in some exercise and sport situations, although this has been less well studied. Lower caffeine doses (1) do not alter the peripheral whole-body responses to exercise; (2) improve vigilance, alertness, and mood and cognitive processes during and after exercise; and (3) are associated with few, if any, side effects. Therefore, the ergogenic effect of low caffeine doses appears to result from alterations in the central nervous system. However, several aspects of consuming low doses of caffeine remain unresolved and suffer from a paucity of research, including the potential effects on high-intensity sprint and burst activities. The responses to low doses of caffeine are also variable and athletes need to determine whether the ingestion of ~200 mg of caffeine before and/or during training and competitions is ergogenic on an individual basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| 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) | 0.004 | 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".