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Recent Developments and Needs in Materials Used for Personal Protective Equipment and Their Testing

2009· article· en· W2014570894 on OpenAlex
Patricia I. Dolez, Toan Vu‐Khanh

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

VenueInternational Journal of Occupational Safety and Ergonomics · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPersonal protective equipmentRisk analysis (engineering)EngineeringClothingEmerging technologiesField (mathematics)Forensic engineeringMaterials testingConstruction engineeringArchitectural engineeringManufacturing engineeringComputer scienceCoronavirus disease 2019 (COVID-19)BusinessMedicine

Abstract

fetched live from OpenAlex

The field of personal protective equipment (PPE) has led to several high technology innovations. Indeed, improved protection against the various possible encountered risks is looked for, in particular at the workplace. This has generated the development of new materials and new manufacturing technologies, as well as the introduction of new applications for existing ones. However, the remaining challenges are numerous. This paper presents some of the new technologies introduced in the field of protective clothing against heat and flames, mechanical risks and chemical aggressors. It also describes new challenges that are currently worked on, in particular the effect of service aging and the need for testing methods that reproduce realuse conditions. Finally, it discusses various existing and potential applications of nanomaterials and smart textiles for PPE.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.314

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)0.0000.000

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 head0.042
GPT teacher head0.286
Teacher spread0.244 · 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