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Record W3185902222 · doi:10.1002/ieam.4505

An integrated knowledge synthesis of regional ambient monitoring in Canada's oil sands

2021· review· en· W3185902222 on OpenAlexaffabout
David R. Roberts, Roderick O. Hazewinkel, Tim J. Arciszewski, Danielle Beausoleil, Carla Davidson, Erin Horb, Diogo Sayanda, Gregory R. Wentworth, Faye Wyatt, Monique G. Dubé

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsCumulative Environmental Management AssociationAlberta Environment and Protected Areas
Fundersnot available
KeywordsStressorGeospatial analysisEnvironmental resource managementEnvironmental monitoringAttributionEnvironmental planningGeographyEnvironmental sciencePsychologyCartography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.268
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2021
Admission routes2
Has abstractyes

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