The influence of land‐use composition on fecal contamination of riverine source water in southern British Columbia
Bibliographic record
Abstract
The potential for riverine drinking source water to become contaminated with pathogens is related to the production and transport of fecal waste from within the local catchment area. Identifying specific relationships between land‐use types and fecal contamination in riverine water provides an indication of the risk associated with land‐use change and helps to target mitigation measures toward land‐use types of concern. Fecal coliform (FC) data from 42 riverine sites across British Columbia (BC), Canada, were examined in relation to land‐use composition (including 16 land‐use types) in the local catchment area. FC concentration significantly increased in relation to anthropogenic land‐use impacts but was negatively associated with undisturbed and high‐elevation land types. Regression tree analysis identified that highest FC concentrations occurred in catchments characterized by more than 12.5% agricultural land and more than 1.6% urban land. Furthermore, the risk of violation of the BC partial treatment raw drinking water quality guideline for FC concentration (100 CFU 100 mL−1) increased in relation to agricultural impacts. Additional factors, such as sewage treatment discharge, low dilution in smaller streams, and higher temperatures, were associated with higher FC concentration among sites with similar levels of agricultural development. These results identify land‐use types that present the greatest threat to riverine contamination, namely agricultural and urban land, and indicate the proportion of such land use associated with high contamination. Land use should be managed and source water protection should be targeted in light of these results so as to minimize the risk of surface water exposure to fecal contaminants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".