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Record W7029938635

Lights Out in the Bakken: A Review and Analysis of Flaring Regulation and Its Potential Effects on North Dakota Shale Oil Production

2014· article· en· W7029938635 on OpenAlexaboutno aff

Bibliographic record

VenueThe Research Repository @ WVU (West Virginia University) · 2014
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsnot available
Fundersnot available
KeywordsOil shaleHydraulic fracturingDirectional drillingDrillingFossil fuelPetroleumNatural gasShale oilUnconventional oil
DOInot available

Abstract

fetched live from OpenAlex

Years ago, there was only darkness on the North Dakota plains.Bismarck, Minot, and Dickinson appeared as small, bright lights to the west and south, bordered by a smattering of Canadian towns to the north.But over a decade later, images from space captured a brilliant area of light in the Williston Basin, glowing brighter than the bustling cities of Minneapolis and St. Paul in neighboring Minnesota.This area of light was not a city, but rather the Bakken shale slay (the "Bakken"), alit by hundreds of drilling rigs and natural gas flares. 2 The prolific escalation of activity in the Bakken is due to the relatively recent technological combination of horizontal drilling and hydraulic fracturing, coupled with high commodity prices.This requisite combination of technology and price permits economic hydrocarbon production of shale reservoirs.The resulting ramp up in shale production has propelled the United States to the top position as the world's largest producer of oil and natural gas. 3 But with this increase in production is a corresponding increase in environmental concerns.Foremost among these concerns is the rise in greenhouse gas ("GHG") and volatile organic compound ("VOC") emissions.Unlike concerns over water contamination or seismic activity by hydraulic fracturing or wastewater injection, which are still mired in controversy and undergoing scientific "Ferg" James, Turner Valley, in IN

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.249
Teacher spread0.229 · 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
Published2014
Admission routes1
Has abstractyes

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