A comparison of Escherichia coli data collected in False Creek by Metro Vancouver and Fraser Riverkeeper
Bibliographic record
Abstract
Background: False Creek is a small inlet centered within Vancouver, British Columbia. Its long and narrow shape facilitates the build-up of contaminants and limits dilution of fresh water. The lack of flushing coupled with sources of fecal contamination results in high levels of Escherichia coli particularly in the summer months. High levels of E. coli in recreational water pose a health hazard to the public. Two organizations Metro Vancouver and Fraser Riverkeeper monitored E. coli levels in False Creek over the 2018 summer season. Methods: Data collected by Metro Vancouver and Fraser Riverkeeper over the 2018 summer season was collected and compared. The secondary data was analyzed from July 8, 2018 to September 29, 2018 from thirty-day geometric means. Each organization sampled on a weekly basis in False Creek, Metro Vancouver sampled from twelve locations and Fraser Riverkeeper sampled from seven locations. Both organizations used similar methodologies in the collection of data with both analyzing for microbiological enumerations of most probable number [MPN] of E. coli per 100/mL samples. All sample sites were divided into three locations representative of False Creek: West, Central and East. The data was then analyzed in terms of overall weekly samples by organization, locational weekly samples by organization and locational weekly samples overall. Results: The data was analyzed using an Aspin Welch Unequal Variance T-test to compare the overall weekly E. coli counts between the organization. Where p = 0.000 and power = 1.00. An Equal Variance T-test was used to compare the locational weekly E. coli counts from the West, Central and East regions of each organization. This yielded a p = 0.000 where power = 1.00. A Kruskal Wallis One-Way ANOVA was used to compare the locational weekly E. coli counts from the West, Central and East regions. This found p = 0.000 and power = 1.00. A MANOVA was used as a reiteration to compare the weekly E. coli counts at each location (West, Central and East) when collected by each organization. This confirmed the same p-value and power results from the three previous tests. Conclusions: There is a statistically significant difference between the two organizations. Not only in overall samples but there is a statistically significant difference between the two organizations when E. coli is amalgamated by location. When accounting for location only, the East region obtained statistically higher E. coli counts as the mean E. coli count for West was 90.8, Central was 248 and East was 1040.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".