Increased organic contamination found on mobile phones after touching it while using the toilet
Bibliographic record
Abstract
Background: Mobile phones are considered as an indispensable handheld item in society today. Frequently used, these devices are a “high-touched” commodity. Previous research has demonstrated that E. coli and other environmental contamination are responsible for the contamination of mobile phones. This study will measure the level of contamination (or sanitation) of mobile phones at an educational institution. Method: The Hygiena MicroSnap Coliform and E. coli Enrichment Swab and the Coliform and E. coli Detection Swabs were used to detect the presence (or absence) of E. coli and total coliforms on subjects’ mobile phones. The Hygiena UltraSnap ATP Surface Test was used to detect levels of ATP. The SystemSURE Plus Luminometer generated readings in RLUs that determined the level of sanitation. In addition, each subject answered two questions regarding their gender and whether or not they have touched their phones while using the toilet within the past week. Results: No presence of E. coli or total coliforms were detected (0 RLUs). A one-tailed paired T-test confirmed that the ATP levels sampled from participants that touched their phones while using the toilet within the past week was statistically significant (P=0.008390). A two-tailed paired T-test confirmed that ATP levels was not statistically significantly different between males and females. Conclusions: Based on the results, touching mobile phones while using the toilet contributes to increased ATP levels found on mobile phones. There were no differences in ATP levels found between males and females. Future studies are required to confirm this.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".