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Record W3111408755 · doi:10.1504/ijhfe.2020.10034343

Effects of chronic cold water exposure in fish harvesters on sensory and motor performance and cold tolerance

2020· article· en· W3111408755 on OpenAlexaff
Heather Carnahan, Emily Walsh, Brianna Walsh, Jillian Holden, Chantel Armstrong

Bibliographic record

VenueInternational Journal of Human Factors and Ergonomics · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFish <Actinopterygii>Cold stressMedicineCold toleranceAudiologyPhysical medicine and rehabilitationToxicologyAnimal scienceBiologyFishery

Abstract

fetched live from OpenAlex

The purpose of this study was to compare how fish harvesters who have years of chronic cold water exposure, differ from controls, with no history of cold exposure, on tests of sensation, motor performance and cold tolerance. Twelve fish harvesters and eight controls performed the following tests in both room temperature and cold conditions: VonFrey touch, grooved pegboard, manual dexterity, maximum cold exposure, and a discomfort rating. During the room temperature test, fish harvesters had a statistically higher VonFrey touch score than controls indicating decrements to their sense of touch. Results showed no statistically significant findings between groups for either of the motor performance, maximum cold exposure or discomfort tests. Findings suggest that chronic cold water exposure does not appear to lead to an adaptation to working in the cold, but instead can lead to non-freezing cold exposure injury.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.012
GPT teacher head0.204
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

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