Caffeine restores engagement speed but not shooting precision following 22 h of active wakefulness.
Bibliographic record
Abstract
BACKGROUND: Current military missions occasionally require combat readiness of soldiers who might be experiencing a sustained period of activity without sleep. Strategies to overcome the debilitating effects of sleep deprivation include the ingestion of caffeine. Unknown is the efficacy of caffeine use on specific elements of target detection and marksmanship following a modest period of sustained wakefulness. METHODS: There were 20 subjects (mean +/- SD of 26.7 +/- 7.2 yr of age, 179 +/- 6 cm in height, and 84.5 +/- 10.8 kg in weight) who participated in double-blind caffeine and placebo trials where each trial involved a 24-h control period (with sleep) followed by 22 h of mixed mental and physical activity with no sleep. At the end of this period, subjects engaged in a 1-h rifle-shooting task. Subjects ingested 400, 100, and 100 mg of caffeine or placebo at 7.5, 3, and 0 h, respectively, prior to shooting. Measures of shooting performance included target engagement time (between target appearance and firing), friend-foe discrimination, accuracy, and precision. RESULTS: Most measures of performance were degraded in the placebo sleep-deprived condition, but only the target engagement time and the number of shots fired were restored by caffeine ingestion. CONCLUSIONS: These findings concur with other research involving different periods of sleep deprivation, and indicate that the cognitive component of the shooting task (i.e., target detection) can benefit from caffeine whereas the psychomotor component (marksmanship) does not. It appears that once the target is detected, the subject is sufficiently aroused to engage the target regardless of the subject's level of alertness prior to detection.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".