Summary: Assessing the public health risks of microbial contamination in recreational waters by satellite imagery
Bibliographic record
Abstract
BACKGROUND: Fecal contamination of recreational waters may lead to gastroenteritis, respiratory infections, dermatitis and ear infections. In addition to directly testing waters for contamination, the World Health Organization (WHO) recommends the assessment of environmental factors known to influence water quality as part of monitoring efforts. Measurement of these factors using satellite imagery may be helpful in Canada where monitoring over large areas or difficult to access locations is needed. OBJECTIVE: To assess the added value of using satellite imagery as part of monitoring and managing microbial risks associated with recreational waters in Canada. METHODS: Satellite images were used to calculate five environmental indices that may affect the risk of contamination of recreational waters: agricultural land, urban areas (impervious surfaces), forest and wetlands. Statistical models including these indices were then compared with the average contamination level of beaches in southern Quebec, Canada. Various satellite sensors were compared against criteria of accuracy and performance. OUTCOMES: Satellite imagery classification performed well for the study area. Two of the variables were significantly associated with higher coliform levels: agricultural land and urban areas. In the context of this assessment, the Landsat-5 sensor offered the best cost-benefit ratio. CONCLUSION: Satellite imagery can be used to identify environmental factors associated with a higher risk of fecal contamination of recreational waters in Canada and may supplement current monitoring and risk assessment efforts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".