Heat strain at high levels does not degrade target detection and rifle marksmanship.
Bibliographic record
Abstract
BACKGROUND: A recent investigation found no degradation in rifle marksmanship due to mild heat strain (up to a 1 degrees C increase in core temperature) even though the subjective sensation of discomfort was significant. The present study was conducted to determine if, and at what level of heat strain would degradation in both target detection and marksmanship (TD&M) occur. HYPOTHESIS: Degradation in TD&M performance is expected in individuals who reach a level of high heat strain. METHODS: There were 11 subjects (mean +/- SD of 28.9 +/- 6.0 yr, 177 +/- 10 cm, and 81.2 +/- 18.8 kg) who participated in 3 counterbalanced trials: control (CN); heat with hydration (HH); and heat without hydration (HD). Core temperature was increased through a combination of exercise and passive heating over 4 h. Measures of shooting performance every half hour included target detection and engagement times, friend-foe discrimination, and shooting accuracy using a small arms trainer. RESULTS: All physiological measures and indices indicated that a significantly elevated heat strain was attained during HH and HD compared with CN. Mean heart rates and core temperatures approached 150 bpm and 39 degrees C, respectively, at the end of the heated trials. Aside from minor differences in target detection and identification owing to target type, no detrimental impact on TD&M due to heat strain, whether hydrated or dehydrated, was observed. CONCLUSIONS: High levels of heat strain do not adversely affect target detection and rifle marksmanship performance, at least for a period of time not exceeding 4 h in a non-threatening environment.
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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 | 0.001 |
| 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".