Wash it, or wear it? Perceptions of odor control technologies on activewear and their influence on the likelihood to launder
Bibliographic record
Abstract
<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;">The consumer phase in the clothing life cycle significantly impacts energy consumption and greenhouse gas emissions. As odor can compel laundering, assumptions that odor-control technologies will reduce laundering frequency can be made. This study examined the effectiveness of antimicrobial (AM) and anti-odor (AO) finishes in reducing washing frequency for activewear. An experimental survey with university students (n=115) was used to test the null hypotheses that AM and AO treatments on activewear would not significantly differ from no odor control treatment. The experimental survey used hypothetical scenarios regarding wear and laundering choices. Results showed no significant differences among treatments in prompting extended wear without washing. Hence, AM- or AO-treated garments did not result in less laundering than untreated activewear. These findings challenge assumptions about the impact of such treatments on laundering habits, highlighting consumers' ingrained laundry practices. <span id="docs-internal-guid-6e32602a-7fff-3e7d-80a7-e50d20fd4001"> <span style="font-size: 10pt; font-family: Calibri, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;"><br></span> </span>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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 teacher head, 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".