Heterotrophic bacteria in botte refill stations
Bibliographic record
Abstract
Background: Heterotrophic bacteria are commonly found in water supplies where there is inadequate or non-existent disinfection. Water coolers are known to have high HPC levels due to the filtered, non-chlorinated water provided. Water bottle refill stations utilize a carbon filter which can act as a food source for HPC. This study measured HPC levels in water samples from bottle refill stations to determine whether there is a difference compared to tap water at BCIT. Method: Standard Method 9060 A was used to collect water from bottle refill stations to compare to non-filtered tap water. Samples were plated using R2A Agar and incubated for 7 days before enumerating HPC from water samples. Samples were collected from specific drinking water fountains that contained Carbon Filters and compared to the nearest tap water source. Results: Mean HPC levels in bottle refill stations were found at 95 cfu/mL while mean HPC levels in tap water were 55 cfu/mL. A two-sample T-test confirmed that the mean HPC levels of bottle refill stations and tap water are statistically significantly different (P= 0.000124). Although the findings were statistically significant, the study’s power was low at 11%. Conclusion: Based on the results, drinking water obtained from bottle-refill stations at BCIT contained on an average higher level of HPC compared to tap water. Overall, HPC levels were below recommended levels in drinking water and not considered to have any harmful effects. To continue the safe use of bottle refill stations, facilities should develop and follow written procedures to maintain stations and ensure regular changing of filters.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".