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Measuring Physiological and Biomechanical Responses to Cold and Icy Conditions: Preliminary Findings

2010· book-chapter· en· W360370700 on OpenAlexaff
Jennifer A. Hsu, Yue Li, Geoff Fernie

Bibliographic record

VenueBENTHAM SCIENCE PUBLISHERS eBooks · 2010
Typebook-chapter
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsHeart ratePaceAmbulatoryEnvironmental scienceMedicineBlood pressureSkin temperaturePhysical medicine and rehabilitationSimulationEngineeringBiomedical engineeringSurgeryGeographyGeodesy

Abstract

fetched live from OpenAlex

Studies conducted in countries all over the world have linked winter climate to increased risks of morbidity and mortality. However, researchers have yet to understand the overall effect of the winter climate including consequences of such factors as surface conditions on the human body. The main objectives of this project were to develop a portable ambulatory system for data collection in cold climates and use it to observe physiological and biomechanical effects of cold temperatures and icy surfaces. Furthermore, a laboratory-based experiment was designed in order to obtain datasets on 11 young and healthy subjects. Participants were observed in a cold chamber walking at a comfortable pace in three test conditions: (1) on ice with a safety harness, and on a rough surface while (2) wearing a harness, and (3) without a harness. The ambulatory monitoring system was used to simultaneously record blood pressure, heart rate, skin temperature, muscle activity, gait parameters and foot pressure, and subjective responses were used to derive a comprehensive understanding of the body's responses. Preliminary findings of this study include results which indicate that heart rate is significantly elevated prior to traversal over ice and blood pressure rises significantly within ten minutes of exposure to cold are also presented.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
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 head0.077
GPT teacher head0.302
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.

Study designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

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