Trace Metal Concentrations in an Intensive Agricultural Watershed in British Columbia, Canada<sup>1</sup>
Bibliographic record
Abstract
Abstract: Effects of agricultural intensification and a naturally occurring landslide of asbestos material upon water and sediment quality in a transboundary watershed were investigated. The water and sediments of the Sumas River watershed were analyzed for copper (Cu), zinc (Zn), chromium (Cr), and nickel (Ni) concentrations in 1993/1994 and 2003/2004 and differences within sites over time were examined. Based upon a review of the literature, Cu and Zn were used as indicators of agricultural impacts while Cr and Ni were used as indicators of impacts from an asbestos landslide. Animal unit equivalents (AUEs) were calculated on a per area basis as an indicator of livestock density using detailed statistical census data. Whatman #42 filtered metals, bioavailable metals, and sediment‐bound metals (in the <63 μm fraction) were determined at 22 sites along the mainstem and tributaries, including two reference sites. Temperature, pH, and dissolved organic carbon (DOC) were also measured. The bioavailable metal fraction was determined using the diffusive gradient thin film technique (DGT). Sediment‐bound results were compared with British Columbia’s Interim Sediment Quality Guidelines (ISQGs) and Severe Effects Levels (SELs). A Wilcoxon signed rank test was used to determine if the concentrations of metals changed significantly within sites between 1993/1994 and 2003/2004. Spearman rank correlation analysis was used to determine relationships between trace metals, water quality parameters, and AUEs/hectare. The results indicate that Cu and Zn levels in sediments increased significantly to concentrations above the ISQGs of 35.7 mg/kg and 123 mg/kg, respectively from 1993/1994 to 2003/2004 in streams, where associated land use was dominated by intensive agriculture. Higher AUEs/hectare were significantly correlated with greater bioavailable levels of Zn as well as higher sediment‐bound Zn concentrations. Neither Cu nor Cr were detected by the DGTs on any of the sampling occasions. The Cr and Ni sediment concentrations were highest in Swift Creek, the headwater tributary affected by the natural landslide of asbestos material, and decreased in the Sumas River downstream from the point of input. Cr and Ni concentrations have increased in the mid‐region of the Sumas River since 1993/1994, suggesting downstream movement of the asbestos material over time. DGT results indicated that bioavailable Zn is significantly positively correlated to sediment‐bound Zn and livestock density, and bioavailable Ni is significantly correlated to sediment‐bound Ni.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".