TRADITIONAL AND SCIENTIFIC KNOWLEDGE OF CONSTRUCTED WETLANDS FOR OIL SANDS PROCESSED WATER REMEDIATION
Bibliographic record
Abstract
The oil sands industry is of enormous economic value to Canada. Despite its contributions, there is limited research on the social, ethical, legal, and cultural impacts of the industry. Remediation processes involve strategies to reduce the availability of soil contaminants and lessen the damage to the health and environment of affected communities. Further, remediation can improve relationships with Indigenous and non-Indigenous communities impacted by the oil sands industry. Advances in remediation have alerted researchers to consider social and cultural preferences in the implementation of remediation technologies. This research explored ways of braiding Indigenous Traditional Knowledge about wetland plants with the current science on treatment wetlands for oil sands processed water. Interviews with local community members and advocacy groups provide preliminary data on preferences for the use of constructed treatment wetland systems and their enhancement using genomics. Data were analyzed using the Biocultural Design Framework and the 10 Principles of Biocultural Conservation. The interview results consisted of four main themes: an ethic of caring for the world, the impacts of the oil sands industry, values associated with constructed wetlands, and the inclusion of Indigenous Traditional Knowledge in science. Results are intended to inform discussions among stakeholders, rights holders, and the Canadian public on the design of constructed treatment wetlands projects for oil sands processed water.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".