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Record W2794114276 · doi:10.11575/prism/26830

Mapping Groundwater Discharges to Rivers near Oil Sands Projects

2017· dissertation· en· W2794114276 on OpenAlexaboutno aff
Jessica Ellis

Bibliographic record

VenuePRISM (University of Calgary) · 2017
Typedissertation
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsGroundwaterPetroleum engineeringGeologyHydrology (agriculture)Environmental scienceMining engineeringGeographyWater resource managementArchaeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Groundwater discharges in the western Canadian oil sands region impact river water quality. Mapping groundwater discharges into rivers in the oil sands region is important to ensure wastewater and steam injections remain sequestered, rather than eventually resurfacing. Saline springs comprised of Pleistocene-aged glacial meltwater enter regional rivers, but their spatial distribution has not been mapped comprehensively. Here we show substantial increases in salinity along three major rivers as they flow through the Athabasca Oil Sands Region adjacent to many active oil sands projects. Major ion concentrations and isotope (2H/1H, 18O/16O, 87Sr/86Sr) compositions suggest that increases in river water salinities are caused by saline groundwater discharges from Cretaceous or Devonian aquifers. These regional subsurface-to-surface connections signify that injected wastewater or steam may potentially resurface in the future, emphasizing the critical import of mapping groundwater flows to understand present-day streamflow quality and to predict potential for injected fluids to resurface.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.128
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.015
GPT teacher head0.231
Teacher spread0.215 · 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 designSimulation or modeling
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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