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Record W2293802244 · doi:10.4236/ojop.2016.51005

Towards Optimizing a Personal Cooling Garment for Hot and Humid Deep Mining Conditions

2016· article· en· W2293802244 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOpen Journal of Optimization · 2016
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsÉcole de Technologie Supérieure
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsHeat stressClothingAir coolingWater coolingActive coolingComputer coolingEnvironmental scienceProcess engineeringMechanical engineeringEngineeringGeologyLaw

Abstract

fetched live from OpenAlex

Workers exposed to hot and humid conditions suffer from heat stress that affects their concentration and can potentially lead to an increase in workplace accidents. To minimize heat stress, protective equipment may be worn, such as personal cooling garments. This paper presents and discusses the performances, advantages and disadvantages of existing personal cooling garments, namely air-cooled, liquid-cooled, phase change, hybrid, gas expansion and vacuum desiccant cooling garments, and a thermoelectric cooling technology. The main objective is to identify the cooling technique that would be most suitable for deep mining workers. It appears that no cooling technology currently on the market is perfectly compatible with this type of mining environment. However, combining two or more cooling technologies into a single hybrid system could be the solution to an optimized cooling garment for deep mines.

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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.056
GPT teacher head0.352
Teacher spread0.295 · 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