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How Clean is Clean Enough? Maintaining Thermal Protective Clothing Under Field Conditions in the Oil and Gas Sector

2004· article· en· W1486993754 on OpenAlexaff
Elizabeth M. Crown, Aifen Feng, Xia Xu

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2004
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLaundryIgnition systemWaste managementClothingCombustionEnvironmental scienceClean-upClean energyPulp and paper industryEngineeringEnvironmental engineeringChemistryChromatography

Abstract

fetched live from OpenAlex

The purpose of this research was to develop practical care procedures to help maintain the protective quality of flame resistant workwear laundered by workers in the field. Based on observed field conditions, experiments were conducted that simulated domestic laundry procedures. The first experiment involved two flame resistant (FR) fabrics, contaminated or not contaminated with oil. Independent variables also included detergent type and laundry pre-treatment. Other laundry parameters were controlled. Results indicated that it is easier to maintain the FR performance of the FR-treated blend than it is for the aramid fabric. It is hypothesized that energy generated by initial ignition of oil on the specimens triggers the FR mechanism of the treatment, which in turn inhibits further combustion. A second experiment using larger specimens and a domestic washing machine also supported the hypothesized mechanism.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.301
Teacher spread0.268 · 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

Citations13
Published2004
Admission routes1
Has abstractyes

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