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Record W2824386828 · doi:10.1680/jenge.17.00110

Environmental implications of end pit lakes at oil sand mines in Alberta, Canada

2018· article· en· W2824386828 on OpenAlexafffundabout
Louis Kabwe, J. D. Scott, Nicholas Beier, G. Ward Wilson, Silawat Jeeravipoolvarn

Bibliographic record

VenueEnvironmental Geotechnics · 2018
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsTailingsOil sandsConsolidation (business)GroundwaterMining engineeringGeologyEnvironmental scienceHydrology (agriculture)Geotechnical engineeringArchaeologyGeography

Abstract

fetched live from OpenAlex

End pit lakes are common at open-pit metal mines around the world and are part of mine closure plans. Those at oil sand mines are no different except that the end pit lakes will be considerably larger, with the area averaging about 4 km2 and reaching up to about 15 km2. In a major study on end pit lakes for oil sand mines, the Cumulative Environmental Management Association defines an oil sand end pit lake as ‘an engineered water body, located below grade in an oil sands post-mining pit’. It may contain oil sand by-product material and will receive surface and groundwater from surrounding reclaimed and undisturbed landscapes. End pit lakes will be permanent features in the final reclaimed landscape, discharging water to the downstream environment. As a permanent feature, the long-term environmental effect of such an oil sand deposit must be carefully designed and monitored. If the end pit lake contains an appreciable thickness of tailings, the consolidation of tailings may continue for many decades. The effects of groundwater leakage are analysed and modelled to show the potentially large amounts of seepage into the underlying stratigraphic units.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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

Citations21
Published2018
Admission routes3
Has abstractyes

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