Impact of land use activities on sediment-associated contaminants; Quesnel River Basin, British Columbia.
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 (QRB) (approx. 12,000 km²) in British Columbia, Canada. Samples of fine-grained sediment were collected monthly during the snow-free season in 2008 using time-integrated 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 μm 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 μm g⁻¹ (As for mining), 5.0 μm g⁻¹ (Se for forestry) and 2192 μm g⁻¹ (P for forestry). 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. Temporal and spatial differences in the metal and P content of fine suspended sediment within the QRB during the 2008 field season were analyzed using rank sum tests in comparison to discharge (Q) and precipitation (PPT) values. Temporal results suggest the overall mining signature was often a function of changes in activity from point sources, while the diffuse sources, forestry and agriculture, were influenced by variations in transport conditions (e.g. PPT and Q). Spatial variation was greatest between mining and control geochemical concentrations. Forestry and agriculture differed for select elements, but played a lesser role than mining. To further characterize the roles of land uses a sediment fingerprinting method was used, involving stepwise discriminant function analysis (DFA). This resulted in a composite signature capable of differentiating correctly 100% of the source geochemical contributions from each land use type. The composite signature was used to determine the basin-scale geochemical signature using a multivariate mixing model. This determined that agriculture was the highest overall contributor of the sediment signature at the outlet in Quesnel. Additionally, the control influence was strongest earlier in the sampling campaign while mining contributed most in the latter sampling periods. This project contributes to the broader science, and future research in the basin, through a study of multiple land uses in a large basin; a different application of the sediment fingerprinting approach; and a contemporary flume-based sampler evaluation.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".