Assessing and monitoring agroenvironmental determinants of recreational freshwater quality using remote sensing
Bibliographic record
Abstract
Diverse fecal and nonfecal bacterial contamination and nutrient sources (e.g. agriculture, human activities and wildlife) represent a considerable non-point source load entering natural recreational waters which may adversely affect water quality. Monitoring of natural recreational water microbial quality is most often based mainly on testing a set of microbiological indicators. The cost and labour involved in testing numerous water samples may be significant when a large number of sites must be monitored repetitively over time. In addition to water testing, ongoing monitoring of key environmental factors known to influence microbial contamination may be carried out as an additional component. Monitoring of environmental factors can now be performed using remote sensing technology which represents an increasingly recognized source of rigorous and recurrent data, especially when monitoring over a large or difficult to access territory is needed. To determine whether this technology could be useful in the context of recreational water monitoring, we evaluated a set of agroenvironmental determinants associated with fecal contamination of recreational waters through a multivariable logistic regression model built with data extracted from satellite imagery. We found that variables describing the proportions of land with agricultural and impervious surfaces, as derived from remote sensing observations, were statistically associated (odds ratio, OR = 11 and 5.2, respectively) with a higher level of fecal coliforms in lake waters in the southwestern region of Quebec, Canada. From a technical perspective, remote sensing may provide important added-value in the monitoring of microbial risk from recreational waters and further applications of this technology should be investigated to support public health risk assessments and environmental monitoring programs relating to water quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".