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Record W1765773821 · doi:10.3233/wor-131690

Subjective and objective analysis of three water pump systems carried by forest firefighters

2014· article· en· W1765773821 on OpenAlexafffundabout
Daniel J. Moser, Ryan B. Graham, Joan M. Stevenson, Patrick A. Costigan

Bibliographic record

VenueWork · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsQueen's University
FundersMinistry of Natural Resources
KeywordsChristian ministryRanking (information retrieval)Rating systemFirefightingAccelerometerSimulationEngineeringComputer scienceEnvironmental scienceArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

BACKGROUND: The Mark 3 (M3) water power pump is an integral piece of wildfire fighting equipment. However, it is provided to fire stations without a carrying harness. The currently-used carrying harness is very uncomfortable, especially when carrying the pumps considerable distance in a forest to reach a water source. OBJECTIVE: The purpose of this study was to advise the Ontario Ministry of Natural Resources on the selection of a new M3 load carriage system. PARTICIPANTS: Twenty Fire Rangers wore the three systems (Original, Prototype, and Modified) through a circuit of tasks representative of their working environment. METHODS: Subjective and objective approaches were combined to assess and rank the M3 carriage systems. Subjective visual analogue scale ratings were obtained for ease of loading/unloading, comfort, system stability, and overall performance. Tri-axial accelerometers were mounted on each pump and at the sternum of each participant to determine relative pump-carrier accelerations. RESULTS: Overall, the Prototype was ranked as the best system; it resulted in the lowest relative pump-carrier accelerations on 10 out of 15 objective measures, and also received a first place ranking on all subjective measures. CONCLUSION: It was recommended that the Prototype be implemented as the M3 carriage system for fire suppression teams.

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)0.001
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 score0.004
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.341
Teacher spread0.316 · 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

Citations4
Published2014
Admission routes3
Has abstractyes

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