One-year trial evaluating the durability and antimicrobial efficacy of copper in public transportation systems
Bibliographic record
Abstract
Surfaces on transit vehicles are frequently touched and could potentially act as reservoirs for micro-organism transmission. Regular cleaning and disinfection to minimize the spread of micro-organisms is operationally challenging due to the need to keep vehicles in circulation. The application of copper (Cu) alloys to high- touch surfaces could help reduce the risk of cross-contamination, however, little is known about the durability and efficacy of engineered copper surfaces after prolonged use. Three Cu products (decal, thermal fabrication, and alloy covers) were assessed over a 12-month period. These Cu products were randomly installed on 110 stanchions on three buses and four train (SkyTrain) cars in Vancouver and three buses, two subway cars, and two streetcars in Toronto with mirrored control surfaces directly opposite. Bacterial counts (Colony forming units, CFU) and ATP bioluminescence (ATPB) were measured every two months after peak morning routes. Durability of the Cu products were assessed monthly through visual inspection and colorimetry assays or by ex-situ microscopy. Cu products on stanchions reduced the mean colony forming units (CFU) of all vehicles by 42.7% in the mean CFU (0.573 (CI 95% 0.453-0.726), p-value < 0.001) compared to control surfaces. The three Cu products exhibited an overall 87.1% reduction in the mean ATPB readings (0.129 (CI 95% 0.059-0.285, p-value < 0.001) compared to controls. Surface Cu concentration for all three products was consistent throughout the 12-month period. Electron microscopy (SEM) and Energy-dispersive X-ray Spectroscopy (EDS) cross-sectional analysis showed no change in thickness or dealloying of Cu products, however SEM top-down analysis revealed substantial carbon accumulation on all surfaces. Cu products installed on transit vehicles maintained antimicrobial efficacy and durability after 12 months of use.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".