Metal uptake in wetland plants from oil sands process-affected waters: a case study
Bibliographic record
Abstract
Treatment wetlands have emerged as a potential remediation option for oil-sands process affected waters (OSPW) which contains a suite of organic and inorganic constituents of potential concern. The aim of this study was to evaluate the fate of metals in a treatment wetland exposed to OSPW. Data was collected over three operational seasons testing freshwater and OSPW inputs at the Kearl Treatment Wetland in northern Alberta. Overall, results show that OSPW from the Kearl oil sands mine has relatively low concentrations of metals and trace elements compared to other industrial OSPW. Of the inorganic constituents introduced into the wetland from OSPW, six analytes (As, Ba, Cu, Mo, Ni, and U) were found to depurate by wetland treatment, were distributed among wetland media (water, sediment, plants), and translocated into water sedge and cattail tissue. Depuration of these analytes from the OSPW occurred mainly through sorption to sediment, while Mo and Cu had higher uptake and storage within plant tissue compared to the other analytes. No significant differences in metal uptake were observed between cattails and water sedge; root concentrations were higher than leaf concentrations. Root and leaf concentration factors were similar across years indicating that mechanisms of plant uptake were not impacted by exposure to OSPW and that bioconcentration was mainly a function of exposure. These findings support continued investigation into the application of treatment wetlands for OSPW remediation and underscore the need for further studies to optimize these systems for diverse OSPW types.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".