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Record W3026056415 · doi:10.1007/s10230-020-00686-7

Effects of Hydrocarbons on Wind Waves in a Mine Pit Lake

2020· article· en· W3026056415 on OpenAlexafffundabout
D. Hurley, Gregory A. Lawrence, Edmund W. Tedford

Bibliographic record

VenueMine Water and the Environment · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaSyncrude
KeywordsTailingsLand reclamationOil sandsGeologyHydrogeologyHydrocarbonOverburdenBiogeochemical cycleEnvironmental scienceAsphaltHydrology (agriculture)Mining engineeringGeotechnical engineeringEnvironmental chemistry

Abstract

fetched live from OpenAlex

Abstract The extraction and upgrading of bitumen to crude oil from the Canadian oil sands has produced large quantities of byproducts such as fluid fine tailings (FFT) and oil sands process affected water (OSPW). One reclamation strategy for these byproducts is to backfill a mined-out pit with FFT and cap it with a mix of OSPW and non-process affected water to form a pit lake. We investigated the effects of hydrocarbons, residual bitumen, on the generation and growth of wind waves, both in the laboratory and in a pit lake. In the laboratory, we compared the wind wave characteristics in the presence and absence of a hydrocarbon film. We showed that the hydrocarbon film dampens high frequency waves, resulting in a slower growing wave field dominated by lower frequency waves. These results were consistent with our field observations. Thus, it appears that the presence of a hydrocarbon film on a pit lake leads to a wind wave field dominated by longer wavelength waves that take more time to develop and grow at a slower rate. This is important since wind wave-driven mixing, in tandem with biogeochemical processes, governs water quality in mine pit lakes. Thus, this work improves understanding of the physical processes that effect water quality in mine pit lakes and enhances the ability of mine managers to conduct pit lake reclamation.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.004
GPT teacher head0.160
Teacher spread0.156 · 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 designBench or experimental
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

Citations3
Published2020
Admission routes3
Has abstractyes

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