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Record W3183091413 · doi:10.24908/pceea.vi0.14842

NON-MEDICAL MASKS: OPPORTUNITIES FOR STANDARDS EDUCATION AND ONLINE DESIGN PROJECTS

2021· article· en· W3183091413 on OpenAlexafffundvenue
David A. Torvi

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsProcess (computing)Sample (material)Test (biology)Computer scienceCoronavirus disease 2019 (COVID-19)Engineering managementEngineering design processInternational standardMeasure (data warehouse)EngineeringMechanical engineeringMedicineTelecommunications

Abstract

fetched live from OpenAlex

While engineering students gain some experience in the use of codes and standards, some may not be exposed to the process used to develop standards, or the history of individual standards. A number of resources on standard development are available to instructors, and knowing the history of a standard will aid in understanding its potential limitations when used in design. This paper will outline how the process of developing standard test methods for non-medical masks during the COVID-19 pandemic can be used as a case study in design courses. Potential online projects and assignments related to testing of these masks are described, including considerations of material performance, comfort and functional fit, along with examples of analysis that students could perform. Sample fabric tests that use readily-available supplies to measure water resistance are described to illustrate how assignments and projects could be completed by studentsremotely in an online course.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2021
Admission routes3
Has abstractyes

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