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Record W3204350883 · doi:10.5194/egusphere-egu21-1407

Intrusions of sediment laden fluids into density stratified water columns can be an unrecognized source of mixing in many lakes.

2021· article· en· W3204350883 on OpenAlexaff
George Lu, Mathew G. Wells, Ian Van Stygeren, Robert E. Hecky

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterflowTurbidity currentArithmetic underflowSedimentGeologyHydrology (agriculture)Stratification (seeds)ConvectionRichardson numberDimensionless quantityEpilimnionDensity contrastSedimentationTurbidityEnvironmental scienceGeomorphologyHypolimnionOceanographyMechanicsStructural basinTurbulenceChemistryGeotechnical engineeringPhysicsGroundwater

Abstract

fetched live from OpenAlex

When a sediment laden river flows into a stratified water body, the water mass can either intrude as an overflow, interflow, or underflow depending upon the density contrast between the river and the lake. If the river is sufficiently warm or fresh to compensate for the additional mass of sediment, an overflow results, below which convective sedimentation occurs. If the sediment load is sufficiently high, then an underflow initially occurs, from which the warm/fresh interstitial material can subsequently loft as sedimentation reduces the initial density. Such convection can even potentially overturn the water column stratification if there is a very fresh, but very high sediment load turbidity current. For intermediate cases, an interflow can occur. Here it is possible for both lofting and sediment driven convection to occur above and below the pycnocline. All these different regimes can be described in terms of two dimensionless parameters: RS and RA, which are ratios that compare the density contrast due to sediment between the river and the upper layer with the density contrast between the upper and lower layers and the density contrast between the river and upper layer, respectively. We used laboratory experiments to describe the vigour of convection in terms of these dimensionless parameters, which then allows the behaviour in various rivers inflows into lakes to be predicted. We also apply our observations to predict how a turbidity current could lead to lofting and possible overturn of the stratification of meromictic Lake Kivu.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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