A Vegetation Assessment of the Kearl Treatment Wetland following Exposure to Oil Sands Process-Affected Water
Bibliographic record
Abstract
Treatment wetlands have emerged as a potential option for the treatment of oil sands process-affected water (OSPW). The long-term viability of these treatment systems relies, in part, on the health and productivity of wetland vegetation. The aim of this study is to investigate the physiological and community-level effects on wetland vegetation at the Kearl Treatment Wetland (KTW) following exposure to different OSPW sources. Annual vegetation assessments were performed during 2016–2021 to monitor species composition, total percent cover, species richness, species morphology (plant stem density, leaf length, and leaf width), and plant vigor in the KTW. Cattail was found to dominate the deep-water zones whereas water sedge was found to dominate the shallow-water zones of the wetland. Species richness in the KTW decreased with time which is typical of constructed wetlands receiving industrial effluents. No changes in plant stem density of cattails or water sedge were observed; however, leaf length decreased from 2019 to 2021, and leaf width decreased from 2020 to 2021. Plant vigor ratings increased in the KTW each year suggesting that the vegetation does not show any major signs of phytotoxicity from OSPW exposure. These results demonstrate the complex dynamics and resiliency of the vegetative community in treatment wetlands, but continued efforts to monitor plant uptake and accumulation are needed to fully assess the phytotoxic effects of OSPW exposure.
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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 | 0.000 | 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) | 0.001 | 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".