An integrated knowledge synthesis of regional ambient monitoring in Canada's oil sands
Bibliographic record
Abstract
Abstract The desire to document and understand the cumulative implications of oil sands (OS) development in the ambient environment of northeastern Alberta has motivated increased investment and release of information in the past decade. Here, we summarize the knowledge presented in the theme-based review papers in this special series, including air, surface water, terrestrial biology, and Indigenous community-based monitoring in order to (1) consolidate knowledge gained to date, (2) highlight key commonalities and gaps, and (3) leverage this knowledge to assess the state of integration in environmental monitoring efforts in the OS region and suggest next steps. Among air, water, and land studies, the individual reviews identified a clear focus on describing stressors, including primarily (1) contaminant emission, transport, transformation, deposition, and exposure, and (2) landscape disturbance. These emphases are generally partitioned by theme; air and water studies focus heavily on chemical stressors, whereas terrestrial monitoring focuses on biological change and landscape disturbance. Causal attribution is often stated as a high priority objective across all themes. However, studies often rely on spatial proximity to attribute cause to industrial activity, leaving causal attribution potentially confounded by spatial covariance of both OS- and non-OS-related stressors in the region, and by the complexity of interacting pathways between sources of environmental change and ecological receptors. Geospatial and modeling approaches are common across themes and may represent clear integration opportunities, particularly to help inform investigation-of-cause, but are not a replacement for robust field monitoring designs. Cumulative effects assessment remains a common focus of regional monitoring, but is limited in the peer-reviewed literature, potentially reflecting a lack of integration among monitoring efforts beyond narrow integrated interpretations of results. Addressing this requires greater emphasis on a priori integrated data collection and integrated analyses focused on the main residual exposure pathways, such as atmospheric deposition. Integr Environ Assess Manag 2022;18:428–441. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). KEY POINTS Our summary of the peer-reviewed literature since 2010 from the Alberta oil sands region reveals an emphasis on chemical stressors and their association with atmospheric emissions, transport, transformation, and deposition, as well as an emphasis on landscape disturbance and associated effects. System-wide gaps in the literature, including topics of concern to local Indigenous communities, derive from a disconnect between theme areas (air, water, land), contribute to an incomplete knowledge of functional linkages, and may undermine the ability to inform regulatory or policy action. Combining papers not explicitly designed together creates interpretative and analytical challenges, and overcoming these may require future optimization of and integration between targeted monitoring projects and entire theme areas.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".