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Record W2025761838 · doi:10.1142/s1464333212500159

ADVANCING A NATIONAL STRATEGIC ENVIRONMENTAL ASSESSMENT FOR THE CANADIAN OFFSHORE OIL AND GAS INDUSTRY WITH SPECIAL EMPHASIS ON CUMULATIVE EFFECTS

2012· article· en· W2025761838 on OpenAlexafffundabout
Sandra Elvin, Gail S. Fraser

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsYork University
FundersIndustry CanadaStrategic Research CouncilBeaufort Regional Environmental Assessment
KeywordsStrategic environmental assessmentCumulative effectsOffshore oil and gasSustainabilitySubmarine pipelineEnvironmental impact assessmentEnvironmental planningBusinessEnvironmental resource managementProcess (computing)WildlifeEnvironmental sciencePetroleum industryEnvironmental protectionNatural resource economicsEngineeringEnvironmental engineeringEcologyEconomicsComputer science

Abstract

fetched live from OpenAlex

Cumulative environmental effects from the offshore oil and gas industry in Canada are inadequately addressed in the Environmental Assessment process due to the complexity of analysis required to overcome unknowns with respect to offshore environments. Limited Environmental Assessments can lead to deleterious consequences for wildlife populations and their habitats dependent on offshore marine ecosystems, particularly as adverse effects become additive and synergistic. A Strategic Environmental Assessment generates guiding policies and programmes for future industrial activities, while taking environmental concerns from all and potential sources into consideration. To address the cumulative impacts of the offshore oil and gas industry in Canada's three marine regions, this paper advances the idea of a National Strategic Environmental Assessment to ensure the health of marine ecosystems is secured throughout present and future industrial activities sustainably.

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 score0.332
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.306
Teacher spread0.291 · 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

Citations21
Published2012
Admission routes3
Has abstractyes

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