Acute caffeine intake before and after fatiguing exercise improves target shooting engagement time.
Bibliographic record
Abstract
INTRODUCTION: Previous research has identified acute caffeine ingestion as an effective aid in counteracting the decline in vigilance experienced during sentry duty and sustained operations. However, further research is needed to clarify caffeine's effects under various stressors and additional operational conditions. The purpose of the present study was to examine the effect of caffeine on target detection and rifle marksmanship during simulated combat operations. METHODS: There were 12 reservists who ingested 5 mg x kg(-1) body mass of caffeine (C) or placebo (P) 1 h before beginning a 2.5-h loaded march and 1.0-h sandbag wall construction task. Following exercise, participants were given a re-dose of 2.5 mg x kg(-1) body mass of C or P. An hour after ingestion, participants commenced a 2.5-h shooting session on a small arms simulator, which included friend-foe discrimination (FF) and vigilance (VIG) tasks. Marksmanship performance measures included engagement time (ET), the number of shots fired (NS), accuracy, and precision. RESULTS: C ingestion (initial and/or redose) did not affect shooting performance during the FF task. ET and NS improved during the VIG task with C ingestion (mean +/- SD of 2.82 +/- 0.27 s and 29.2 +/- 1.9 shots out of 30 targets, respectively) compared with the P trial (3.00 +/- 0.26 s and 28.0 +/- 3.0 shots; p < 0.05). CONCLUSION: Caffeine ingestion improves target detection and engagement speed during vigilance situations, but is not effective during more complex operations requiring higher levels of cognitive processing and fine motor control and coordination.
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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".