1 Title: Drinking water quality and well owner perceptions of quality in a rural watershed in British Columbia, Canada.
Bibliographic record
Abstract
In rural areas individual well owners have little control over their drinking water quality, which can be influenced by geological factors and adjacent land use. Analysis of groundwater used for drinking in a rural watershed with mixed land use in British Columbia, Canada, showed that although most samples did not exceed guidelines designed to protect human health, those that did were concentrated in urbanising areas. With the help of local volunteers, 75 groundwater samples were collected in the Hatzic Valley, located in the Lower Fraser Valley area of British Columbia. Collection and analysis occurred twice, once in July 2002 and again in March 2003. A spatial database was created and the percentages of four predominant land uses were calculated within several radii of the wells sampled. Two wells exceeded the health standard for nitrate-N and 10 more had nitrate-N levels above 3 mg/L (considered indicative of land use impacts). All the wells with elevated NO3-N were shallow (less than 10 m deep). Percent forest cover surrounding the wells was negatively correlated with nitrate level, while percent urbanised land was positively related to nitrate. The cause of high nitrate in the urban areas is thought to be from septic systems. A questionnaire study revealed that the majority of local residents perceived their water quality to be good or excellent. The residents base their perceptions on tangible indicators rather than chemically determined ones. The negative perceptions were correlated with the presence of high levels of iron and manganese rather than nitrate.
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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 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.006 | 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".