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Record W2520547602 · doi:10.1177/1541931213601161

A Preliminary Investigation of the Effect of Protective Clothing Weight, Bulk and Stiffness on Combat Mobility Course Performance

2016· article· en· W2520547602 on OpenAlexaff
Linda Bossi, Monica L. H. Jones, Alison Kelly, David W. Tack

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsArmourClothingStiffnessCourse (navigation)EngineeringStructural engineeringForensic engineeringMaterials scienceComposite materialAerospace engineeringPolitical science

Abstract

fetched live from OpenAlex

Soldier loads continue to rise in response to new technological capabilities and emerging threats. However, literature addressing the extent to which load mass properties affects operational task performance and mission outcome is sparse. The objective of this preliminary study was to quantify the effect of PPE mass properties (weight, bulk and stiffness) on combat mobility, as measured using the standardized Load Effects Assessment Program (LEAP) course. Twenty-four soldiers completed the LEAP course in three clothing and individual equipment (CIE) configurations (UE: unencumbered; FFO: full fighting order (FFO) without body armour; and FFO+: FFO with body armour). Significant differences between clothing conditions were revealed for LEAP performance metrics (overall course time). Regression analysis revealed significant relationships between overall mobility performance and condition mass properties of weight, bulk, and stiffness. Outcomes will influence the design of future CIE and future research in this area.

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 score0.020
Threshold uncertainty score0.755

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 studies0.0010.000
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 head0.022
GPT teacher head0.307
Teacher spread0.284 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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