Impact of land use activities on fine sediment-associated contaminants, Quesnel River Basin, British Columbia, Canada
Bibliographic record
Abstract
The impact of various land use activities (forestry, mining, and agriculture) on the quality of fine- grained sediment (<63 µm) was investigated in the Quesnel River Basin (approx. 12 000 km 2 ) in British Columbia, Canada. Samples of fine-grained sediment were collected monthly during the snow-free season in 2008 using time-integrated isokinetic samplers at sites representative of forestry, mining, and agricultural activities in the basin. Samples were also collected from replicate control sites that had undergone limited or no disturbance in recent years, and also from the main stem of the Quesnel River. Generally, metal and nutrient concentrations for impacted sites were greater than for control sites. Concentrations of As (mining sites), Cu (forestry sites) and Zn (forestry sites) were close to or exceeded upper Sediment Quality Guideline (SQG) thresholds, while Se concentrations for mining sites were elevated and within the range cited for contaminated environments. Phosphorus values were generally <1000 μg g -1 for all land use activities and below available SQGs. Values for individual samples were, however, greater than upper SQG levels, such as 22.7 μg g -1 (As), 801 μg g -1 (Cu), 5.0 μg g -1 (Se) and 2192 μg g -1 (P). These preliminary results suggest that metal mining and forest harvesting are having a greater influence on the concentration of sediment- associated metals and nutrients in the Quesnel basin, than agricultural activities.
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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 | 0.003 | 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) | 0.003 | 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".