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Record W2548370312 · doi:10.29173/alr463

Regulating Hydraulic Fracturing: Regulatory Recourse for Subsurface Communication

2016· article· en· W2548370312 on OpenAlexvenueaboutno aff
Kimberly A. S. Howard

Bibliographic record

VenueAlberta Law Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic fracturingHarmJurisdictionProduction (economics)Order (exchange)LiabilityBusinessCompensation (psychology)LawPetroleum engineeringGeologyEconomicsFinancePolitical science

Abstract

fetched live from OpenAlex

This article provides an overview of the legal framework for the regulation of hydraulic fracturing in Alberta and examines the potential regulatory options and liability for subsurface reservoir communication caused by hydraulic fracturing activities. Specifically, this article examines the jurisdiction of the Alberta Energy Regulator (AER or the Board) to: (1) order that operations be shut-in or suspended due to subsurface reservoir communication; (2) impose obligations on industry to provide notification of hydraulic fracturing activities, including subsurface reservoir communication; (3) order mandatory commingling orders; (4) encourage production sharing agreements; and (5) impose testing, monitoring, production controls, and reporting obligations.With the widespread use of multistage horizontal hydraulic fracturing, disputes related to subsurface communication will continue to be raised with the AER and in the courts. Thus far, the AER has taken a risk management approach through monitoring and testing requirements. Generally, the AER has permitted development to occur by endorsing an approach which relies on the known and inevitable consequences of mining and recovering the minerals. This approach has been justified by the AER on the basis that any production of another party’s minerals does not result in irreparable harm. The harm or damage caused can be identified, quantified, and compensation paid.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.031
GPT teacher head0.328
Teacher spread0.297 · 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 designNot applicable
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

Citations1
Published2016
Admission routes2
Has abstractyes

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