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Record W7065598365

Environmental Assessment as a Tool for Managing Impacts on Wetlands: Understanding Current Practice in the Mining Sector

2023· dissertation· en· W7065598365 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsWetlandResource (disambiguation)HabitatWetland conservationNatural resourceEnvironmental impact assessmentProxy (statistics)Water resources
DOInot available

Abstract

fetched live from OpenAlex

Wetlands are ecologically rich lands but also heavily impacted. Canada has a large mining sector, with operations impacting the important functions provided by wetlands that benefit humans. Environmental assessment (EA) is the primary regulatory tool for mitigating the impacts of development, including mining, to wetlands. Many jurisdictions in Canada use a hierarchical approach to mitigate wetland loss through avoidance, minimization, restoration. Any remaining loss is offset through compensation as under current federal and provincial policies or protections, development activities should pose “no net loss” of wetland functions. Although the mining industry is an important natural resource sector in Canada, there is limited research on how the potential impacts of mining activity on wetlands are identified and managed through EA processes. In response, this research examined wetland impacts and mitigations in EA. Case studies of mining projects in British Columbia (BC) and Yukon (YT) were examined, as mining is particularly important to the economies of this province and territory, and often occurs in areas of high wetland density. The methodology consisted of an in-depth document analysis of mining project EAs. The results indicated that, in BC, the EA practice tends to default to wetland area as a proxy for wetland function and is the primary measure for assessing impacts to wetlands. There is strong focus on direct impacts, while insufficiently describing baseline wetland functions potentially impacted and to be mitigated. Hydrological and habitat wetland functions were prioritized when described in mitigation measures. In YT, the reviewed EAs contain no information on the impacted wetland area, wetland class, or wetland functions, nor provide information on how the proposed mitigation measures would address potentially impacted wetland functions. The often-poor linkages between proposed wetland mitigation measures and identified project impacts found in this research were attributed to inadequate wetland policies and regulations for mitigating impacts, and poor EA practices to address and mitigate wetland impacts effectively. An exploration of mitigation practices across jurisdictions exposes inconsistencies within the implementation of the mitigation hierarchy, with a focus on minimization in BC and restoration in YT. Compensations approaches, only identified in BC, were creation, enhancement, and off-site restoration. While wetland loss in YT is inconclusive due to information gaps, the EA practice in BC suggests that the mitigation hierarchy is not fully applied, and the province is therefore likely moving toward a net wetland loss. Understanding and addressing the issues highlighted by this thesis will be important to advancing the effectiveness of EA to manage the impacts of mining activities on wetlands.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.498
Threshold uncertainty score0.990

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.008
Science and technology studies0.0030.011
Scholarly communication0.0110.006
Open science0.0020.003
Research integrity0.0020.003
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 head0.017
GPT teacher head0.228
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes2
Has abstractyes

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