MétaCan
Menu
Back to cohort
Record W6926021594 · doi:10.20381/ruor-22713

Ensuring health and environmental protection in hydraulic fracturing: A focus on British Columbia and Alberta, Canada

2018· article· en· W6926021594 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Ottawa - Library · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRisk managementHuman healthNatural resourceBest practiceInternational communityRisk assessment

Abstract

fetched live from OpenAlex

Unconventional natural gas resources recovered using hydraulic fracturing (HF) is contributing to national energy self-sufficiency and could be a significant factor in the global transition to a low carbon economy. Using an integrated risk management framework, we conduct a comparative analysis of practices and review recommendations of a regulatory, economic, advisory, community-based, or technological nature for British Columbia and Alberta, Canada. Lessons learned from international assessments of risk issues are also considered. Overall, there is much less emphasis on potential impacts on human health than on the environment. The analysis also identifies a need for a strong and adequately resourced regulatory framework that works in concert with enhanced technological requirements; evidence-based emissions standards; regulated and/or community-based setbacks and buffer zones; operational surveillance, reporting, and disclosure of value-chain activities in an accessible and transparent way; community participation in the development of these mechanisms; and provision for legacy sites. Economic options such as performance-based taxes and fees, industry-funded studies, the role of carbon taxes, and cost allocations to protect or improve determinants of health are the least advanced option. This analysis provides support for the development of a risk management policy agenda with respect to broad and persistent HF risk management issues.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.003
GPT teacher head0.132
Teacher spread0.129 · 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.

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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of Ottawa - LibrarySame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207