Chemical speciation and partitioning of trace metals (Cd, Co, Cu, Ni, Pb) in the lower Athabasca river and its tributaries (Alberta, Canada)
Bibliographic record
Abstract
Concentrations of Cd, Co, Cu, Ni and Pb were measured in particulate and dissolved phases at 11 sites located upstream and near Athabasca oil sands development. The in situ discrimination between non-labile and labile dissolved metals was done using diffusive gradients in thin-films (DGT) devices. The DGT-labile fraction of Co and Ni was 30% lower near development sites whereas Cu, Cd and Pb showed minor changes spatially. It was found that an 8-fold increase in dissolved organic matter (DOM) near development induced a rapid decrease in DGT-labile metals. Dissolved metal concentrations were used along with DOM, major ions, nutrients, pH and conductivity to calculate the distribution of dissolved metal species using the speciation model WHAM. Labile-DGT metal concentrations agreed well with WHAM-predicted concentrations. It was also found that a significant amount of metals were associated with the non-DGT labile fraction (i.e. colloidal DOM) and colloid abundance was more important than suspended particulate matter abundance in influencing metal mobility near Athabasca oil soils development. Since changes in colloidal DOM levels are likely to be the result of surface mining activities, this confirms the serious effects of oil sands activities on metal biogeochemical cycles in the lower Athabasca River.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".