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Record W3087018802 · doi:10.11575/prism/38186

Climate Change in the Canadian Impact Assessment Process

2020· dissertation· en· W3087018802 on OpenAlexaboutno aff
Adewale Oluwapelumi Ajayi

Bibliographic record

VenueOpen MIND · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeProcess (computing)ClimatologyEnvironmental scienceEnvironmental resource managementGeographyEnvironmental planningComputer scienceGeologyOceanography

Abstract

fetched live from OpenAlex

This thesis examines how climate change, particularly upstream and downstream greenhouse gas emissions (GHGs), have been considered in Canadian environmental impact assessment (EIA) of energy projects. The legal and policy framework on EIA for energy projects has evolved, and the recent transition from the Canadian Environmental Assessment Act 2012 (CEAA 2012) regime to the Impact Assessment Act (IAA) is the most recent change. Although the CEAA 2012 and the IAA share similarities, they have different requirements with respect to GHG emissions. One of the major differences is that the IAA makes climate change considerations an essential factor for the assessment and decision-making phases of the review and approval of a proposed energy project. Under CEAA 2012, climate change considerations were not clearly spelt out, though there were several avenues for GHGs to be considered in the assessment process. This thesis reviews the former regime and practice under CEAA 2012, then examines the new regime, and the GHGs consideration in the EIA process in the United States of America (U.S).

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)0.026
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score0.907

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.009
Science and technology studies0.0280.012
Scholarly communication0.0220.006
Open science0.0030.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.002

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 head0.047
GPT teacher head0.407
Teacher spread0.360 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicEnvironmental and Social Impact AssessmentsFrench-language works237,207