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Record W3206036846

Energy intake, weight, and body composition of Canadian soldiers participating in an Arctic training

2021· article· en· W3206036846 on OpenAlexaffvenueabout
Florence Lavergne, Raphaëlle Laroche-Nantel, Denis Prud’homme, Isabelle Giroux

Bibliographic record

VenueJournal of military and strategic studies · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversité de MonctonUniversity of Ottawa
Fundersnot available
KeywordsBody weightTraining (meteorology)Composition (language)ArcticEnergy requirementWeight lossPsychologyGerontologyEnvironmental healthMedicineDemographyAnimal scienceGeographyMeteorologyObesityBiologySociologyEcologyEndocrinology
DOInot available

Abstract

fetched live from OpenAlex

Military training in extreme environments increases weight loss risk, which could affect soldiers’ performance. This study aimed to assess daily energy intake and requirements of Canadian Armed Forces soldiers consuming combat rations over an 8-week Arctic training and document impact on weight and body composition. Fourteen soldiers participated (males; 31.3±5.5 years). Body weight and composition were measured at different training time-points. Energy intake was measured using food diaries. Energy requirements were estimated using a predictive equation developed for the military. Fourteen soldiers participated. Weight loss was measured (3.9±3.0 kg) for 12 participants from beginning to mid-training after a week consuming combat rations. Energy intake on rations was lower than estimated daily energy requirements and resulted in energy deficits (49.8±19.2%). No body composition changes were measured. More research is needed to explore ways to make rations better adapted to reduce energy deficit and maintain soldiers’ body weight in extremely cold environments.

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 score0.140
Threshold uncertainty score0.997

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 head0.200
GPT teacher head0.424
Teacher spread0.224 · 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

Citations0
Published2021
Admission routes3
Has abstractyes

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