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Record W2884030422 · doi:10.25316/ir-625

Case study analysis on the impacts of surface water allocations for hydraulic fracturing on surface water availability of the upper Athabasca River

2018· article· en· W2884030422 on OpenAlexfundaboutno aff
Alison MacQuarrie Tindle

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
FundersRoyal Roads University
KeywordsHydraulic fracturingSurface waterEnvironmental scienceHydrology (agriculture)Petroleum engineeringGeologyWater resource managementGeotechnical engineeringEnvironmental engineering

Abstract

fetched live from OpenAlex

The Duvernay Formation of the Western Canadian Sedimentary Basin underlies portions of the Upper Athabasca Watershed. To access unconventional shale resources in the Duvernay Formation, horizontal drilling and hydraulic fracturing were introduced to the area. Hydraulic fracturing requires large volumes of surface water for enhanced completions. This study examines the impacts of surface water allocations, as determined by the Alberta Desktop Method, on water availability of the Upper Athabasca Watershed, under the conditions of global climate change. Results of this study find most water allocations issued through temporary diversion licenses meet the constraints of the Alberta Desktop Method. The greatest risk for water imbalance scenarios occurs during winter months when historical surface water flows measure the lowest. Findings of this research will assist decision makers in understanding current and future water balance scenarios, and in determining appropriate and sustainable water management techniques for hydraulic fracturing operations throughout the Duvernay Formation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.487
Threshold uncertainty score0.980

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.199
Teacher spread0.191 · 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 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

Citations2
Published2018
Admission routes2
Has abstractyes

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