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Record W4308627936 · doi:10.1080/14615517.2022.2135232

Lack of consideration of ecological connectivity in Canadian environmental impact assessment: Current practice and need for improvement

2022· article· en· W4308627936 on OpenAlexafffundabout
Charla Patterson, Felipe Casasanta Mostaço, Jochen A.G. Jaeger

Bibliographic record

VenueImpact Assessment and Project Appraisal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsConcordia University
FundersConcordia University
KeywordsEnvironmental impact assessmentCurrent (fluid)Environmental resource managementEnvironmental planningEnvironmental scienceImpact assessmentEcologyPolitical scienceOceanographyGeologyBiology

Abstract

fetched live from OpenAlex

This study seeks to understand the extent to which ecological connectivity has been considered in EIA in Canada. Several factors that may influence the consideration of connectivity were analyzed in an evaluation of 14 environmental impact statements (EIS) obtained from the Canadian Impact Assessment Registry. Connectivity is largely absent from the EIA process, and even projects that attempted to consider connectivity lacked the rigor required to effectively assess impacts on connectivity. Projects that included connectivity as a valued component performed somewhat better, whereas the assessment of connectivity was not affected by different federal environmental acts (CEAA 1992 vs. CEAA 2012), development sectors, or proponent types. Between sections of the EIS, a significantly greater number of evaluation criteria were met in the scoping section compared to all other sections. Without adequate guidance, connectivity analysis in EIA has been conducted ad hoc, with considerable variation in quality. Including connectivity consideration in EIA legislation would provide a legal framework to address the lack of policies, standards, and assessment guidelines. We provide recommendations for integrating connectivity in EIA in Canada and elsewhere.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.430
Teacher spread0.393 · 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 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

Citations11
Published2022
Admission routes3
Has abstractyes

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