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Record W2227033744 · doi:10.12697/akut.2015.21.06

Physical fitness characteristics of a front-line firefighter population

2015· article· en· W2227033744 on OpenAlexaffabout
Michael R. Antolini, Zach Weston, Peter M. Tiidus

Bibliographic record

VenueActa Kinesiologiae Universitatis Tartuensis · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPhysical fitnessTest (biology)PopulationFront lineComputer scienceApplied psychologyPhysical therapyPsychologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Firefighters require a high level of physical fitness in order to meet the demands of their profession. While physical performance testing is required to join the department, firefighters are not subject to further formal exercise or performance testing throughout the duration of their careers. The purpose of the present study was to gather information regarding the physical fitness of front-line Canadian firefighters, to determine whether a testing battery predictive of both performance and future injury risk is viable, and to make recommendations regarding the format of fitness testing and training programs for front-line firefighters. Front-line, career firefighters were tested on a variety of physical fitness measures related to body composition, strength, power, and endurance over three testing sessions. Large ranges of data were found for many of the measures taken and tests performed. Body fat percentage had the most significant correlations with other performance tests while performance in the pushup test and vertical jump correlated strongly with many of the more sophisticated fitness tests. Some firefighters may not possess adequate fitness levels to optimally perform their job responsibilities. Simple field tests may form the basis of predictive testing batteries for both fitness and future injury risk, though further research is needed.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.376
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2015
Admission routes2
Has abstractyes

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