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Record W2398871119 · doi:10.1080/14615517.2016.1176405

Unconventional shale gas development: challenges for environmental policy and EA practice

2016· article· en· W2398871119 on OpenAlexaff
Nonita T. Yap

Bibliographic record

VenueImpact Assessment and Project Appraisal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHydraulic fracturingUnconventional oilEnvironmental planningAquiferEnvironmental impact assessmentCumulative effectsShale gasEnvironmental scienceEnvironmental resource managementGroundwaterBusinessOil shalePetroleum engineeringGeologyPolitical scienceEcologyWaste managementEngineering

Abstract

fetched live from OpenAlex

The growth of unconventional shale gas development has been accompanied by controversy over its environmental and social impacts. This paper reviews recent literature to clarify what is known and not known about the physical, chemical and toxicological properties of the process chemicals and wastewaters generated in hydraulic fracturing, the mechanisms and pathways by which they enter surface water and groundwater aquifers and the risks posed to human and ecosystem health. Assessing the impacts of unconventional shale gas development is clearly constrained by a lack of baseline information, complex hydrogeological histories for natural migration of hydrocarbons, lack of tracers to monitor and verify the source, timing and mechanism of contaminant migration into water resources. This is compounded by lack of transparency and accountability in policy decisions. The paper argues that managing the social and environmental risks of unconventional shale gas development calls for a new generation of impact assessment, one that marries the ideals of strategic environmental assessment, cumulative effects assessment, backcasting and deliberative and inclusive processes of community engagement towards collective risk management.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.807

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.356
Teacher spread0.334 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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