The efficacy of ATP removal on gym contact surfaces with disinfectant wipes
Bibliographic record
Abstract
Background: Gym equipment surfaces are known to harbor a range of contaminants due to the wide range of community use of the equipment. Certain gym equipment undergoes daily sanitation, however many other equipment surfaces do not. This study measures the levels of contamination on certain gym equipment surfaces at an educational institute gym facility and determines the contamination levels after disinfectant wipes are applied. Methods: The method to obtain the data was determined by the use of the Hygiena Systemsure II ATP analyzer in conjunction with Hygiena Ultrasnap ATP surface swabs. Gym equipment (barbells, dumbbells, machine handles, cable attachments) and other surfaces (benches, floor mats) were swabbed subsequently after a random gym patron had used the equipment to capture an accurate representation of the cleanliness of the surfaces. Disinfectant wipes were then applied to the same area before being swabbed again to determine contamination levels after disinfection. Results: The results demonstrated a statistically significant difference in the reduction of ATP levels with the use of disinfectant wipes with a p-value of 0.00001 at α=0.05. Alpha error was highly unlikely with a p-value being that low. Power was 99.9%, therefore there is a strong likelihood that we are correctly rejecting the null hypothesis. Conclusion: The study can conclude that disinfectant wipes do make a significant difference in surface cleanliness levels. Equipment that does not undergo routine cleaning such as the equipment used by the hands carry a much higher contamination rate than the body contact surfaces. Gym patrons should disinfect all body contact surfaces prior to use to reduce the risk of getting an infectious disease.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".