Developing Comfortable Cloth Face Masks: An Experiment with Four-Ply Cloth
Bibliographic record
Abstract
The public usage of disposable masks is indeed practical to reduce the spread of covid-19, but they generate unrecyclable waste.This research aims to develop a pattern of cloth masks comfortable enough for citizens to use in their any activity to minimize plastic waste of disposable masks by switching to cloth masks.The method of this research is research and development (R & D) adopting seven of the ten steps of Borg and Gall's model.Those seven steps include research and information collecting, planning, develop preliminary of product, preliminary field testing, main product revision, main field testing, and product revision.The data of this research are the quantitative data collected by distributing questionnaires to respondent users and the qualitative ones gained by holding interviews with the subjects in the main field testing.Subsequently, the techniques for data analysis are interactive model for qualititative data and the descriptive analysis for quantitative data.The findings show that after stages of revision and modification, cloth masks can offer users comfort that they need and seek.The development of the products is based definitely on users' complaints and suggestions on what part of cloth masks, be it the main part or the supporting one, causes them to experience discomfort in their indoor or outdoor activities.This research only focuses on the level of comfort users feel while speaking and breathing inside cloth masks.Therefore, future research needs to focus on other properties of cloth masks such as their absorption and their filtration efficiency after being washed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".