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Record W2137272106 · doi:10.1177/0040517514542864

Thermal sensors for performance evaluation of protective clothing against heat and fire: a review

2014· review· en· W2137272106 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.

Bibliographic record

VenueTextile Research Journal · 2014
Typereview
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClothingTextileHeat fluxHazardous wastePersonal protective equipmentArchitectural engineeringThermalEnvironmental scienceMechanical engineeringComputer scienceEngineeringForensic engineeringAutomotive engineeringHeat transferMaterials scienceWaste managementCoronavirus disease 2019 (COVID-19)Composite material

Abstract

fetched live from OpenAlex

Many thermal sensors can simulate and predict the heat flux transmitted through human bodies under hazardous fire exposures. These sensors are usually used to evaluate the thermal protective performance of firefighters’/industrial-workers’ clothing. This paper presents a thorough review on the latest thermal sensors and their applications in evaluating the performance of protective clothing. Several important aspects associated with the sensor development – constructional features, working principles, and characteristics – were discussed and their applications in protective textile materials testing were summarized. The application procedures of sensors both in heat source calibration and heat flux measurement were introduced. Finally, several research cases were explored. This review could help in understanding basics of the current thermal sensors and their nature in testing protective clothing performance.

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)0.013
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.319
GPT teacher head0.521
Teacher spread0.202 · 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