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Record W4388105885 · doi:10.1080/17480930.2023.2270301

Climatic controls on the water balance of a pilot-scale oil sands mining pit lake in the Athabasca oil sands region, Canada

2023· article· en· W4388105885 on OpenAlexafffundabout
Austin Zabel, Scott J. Ketcheson, Richard M. Petrone

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2023
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsAthabasca UniversityUniversity of Waterloo
FundersSuncor Energy Incorporated
KeywordsOil sandsHydrology (agriculture)Environmental scienceWater balanceSurface miningGroundwaterTailingsWater tableSurface waterLand reclamationGeologyGeographyEnvironmental engineering

Abstract

fetched live from OpenAlex

Energy companies in the Athabasca Oil Sands Region in Alberta, Canada are evaluating the viability of incorporating pit lakes into reclamation closure designs to both sequester tailings and re-integrate the mining lease into the broader natural landscape. Lake Miwasin is a pilot-scale oil sands pit lake encompassed by a constructed catchment where the volume of the water cap is not actively managed. This study compared the water balance during the open water season over two consecutive years with contrasting levels of summertime precipitation. The first year had above average rainfall triggering surface water inflow events that diluted the over-winter water volume by ~ 25%. Flushing of the deepest layers of the water column was restricted from May – September as thermal stratification inhibited complete lake mixing. The second year had below average rainfall resulting in minimal surface water inflow and a drop in lake stage of ~ 30 cm. As the constructed catchment lacks both natural water bodies and connectivity to a legacy groundwater system, freshwater additions to the lake during the summer season were governed by rainfall. This research highlights challenges with constructing sustainable reclamation landscapes within a region characterised by variations in interannual and decadal water cycles.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations5
Published2023
Admission routes3
Has abstractyes

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