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Record W93080177

A Study of the Relationship Between Foot Size and Combat Boot Size in the Canadian Forces

2000· article· en· W93080177 on OpenAlexaboutno aff
Walter R. Dyck

Bibliographic record

VenueDefense Technical Information Center (DTIC) · 2000
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsInfantryFoot (prosody)Boot campDimension (graph theory)PopulationMathematicsStatisticsEngineeringSimulationComputer scienceDemographyLawPolitical scienceArtSociology
DOInot available

Abstract

fetched live from OpenAlex

Foot and boot size data were collected on 825 individuals (70 females and 755 males) predominantly Canadian Forces (CF) Land Force (LF) infantry. The differences between foot-plus-sock dimensions and boot dimensions were determined and the results indicate that 227 personnel were wearing the predicted length of boot, 217 were wearing the predicted width of boot, and only 58 were wearing the predicted length and width of boot. The data suggests that priority is given to finding the best fit in the width of a boot and then accepting the best length available in that width. A large number of individuals, however, cannot find a boot that fits properly or do not know what constitutes a good fit and thus must compromise on at least one dimension, usually resulting in wearing a boot that is too long. A new sizing system for boots, which is better correlated to the foot dimensions of the CF LF population, is required. The numerous occurrences of very large differences between boot fit dimensions and foot-plus-sock measurements indicate that many personnel have not been fitted properly. Since these large differences exist for all lengths and widths, a much better fit was theoretically available for many. Soldiers admit there is not enough effort expended to achieve a good fit, a deficiency that can be overcome with minimal training and patience.

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)0.001
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 score0.116
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.102
GPT teacher head0.399
Teacher spread0.298 · 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

Citations2
Published2000
Admission routes1
Has abstractyes

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