Physiological demands and physical performance determinants of a new firefighting simulation test
Bibliographic record
Abstract
The study aimed to examine physiological responses of firefighters performing a firefighting simulation test (FST) and to determine the relationship between physical fitness parameters and FST performance. Aerobic fitness, muscular strength, muscular endurance, and anaerobic capabilities were evaluated in 37 firefighters (21-profesionals and 16-volunteers firefighters). Furthermore, participants carried out the FST during which we measured performance, respiratory gas exchange, heart rate (HR), perceived exertion and blood lactate concentrations. Males were significantly faster than females for all tasks of the FST (p < 0.01); however, final performance score (16.5 ± 2.9 and 14.5 ± 2.6 points for males and females, respectively), HR (94.0 ± 2.0% and 93.7 ± 2.3% of HRmax) and perceived exertion (8.1 ± 0.9 and 7.1 ± 1.3) were not significantly different. Prediction of FST performance by LASSO regression revealed a model that included mainly aerobic capacity and maximal strength. In conclusion, FST challenged both aerobic and anaerobic energy metabolisms for both males and females and requires various physiological abilities to perform. Practitioner Summary: For the safety of firefighters and victims, firefighters must meet minimum physical requirements. 37 firefighters performed physical tests and a new firefighting test implemented for the recruitment of firefighters in France. The results revealed that this test is strenuous and that performance is associated with cardiorespiratory fitness and muscular strength. Abbreviations: V̇O2: oxygen consumption; V̇CO2: carbon dioxyde production; V̇E: expired ventilation; RER: respiratory exchange ratio; FST: firefighting simulation test; MAS: maximal aerobic speed; HR: heart rate; RPE: rating of perceived exertion; MVC: maximum voluntary contraction; IMTP: isometric mid-thigh pull; TTE: time to exhaustion; Ppeak: peak power; Pmean: mean power; LASSO: least absolute shrinkage and selection operator; La-: blood lactate concentration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".