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Record W2082405452 · doi:10.4319/lo.2013.58.1.0153

Internal hydraulic jumps in a long narrow lake

2012· article· en· W2082405452 on OpenAlexaff
Abbas Dorostkar, Leon Boegman

Bibliographic record

VenueLimnology and Oceanography · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsHydraulic jumpSeicheBaroclinityFroude numberGeologyInternal tideHydraulicsSurgeNonlinear systemInternal waveSillJumpMechanicsForcing (mathematics)GeomorphologyAtmospheric sciencesPhysicsClimatologyOceanographyBreakup

Abstract

fetched live from OpenAlex

High‐resolution three‐dimensional numerical modeling and field observations were used to describe the nonlinear response of Cayuga Lake to surface wind forcing. The degeneration of the basin‐scale internal wave field was characterized according to the composite Froude number ( G 2 ), Wedderburn number ( W N ), and Lake number ( L N ), which are measures of hydraulic control and bulk and integral wind disturbance force to the baroclinic restoring force, respectively. The typical Cayuga Lake response was a nonlinear surge when ∼ 1 < W N (or L N ) < ∼ 2–12 and a surge with emergent nonlinear internal waves when W N or L N < ∼ 2, in agreement with published laboratory studies. An observed shock front was simulated to be an internal hydraulic jump, occurring at midbasin during strong winds when W N < 0.8. To our knowledge, this is the first simulation of a midbasin seiche‐induced hydraulic jump (supported by field data) due to supercritical conditions ( G 2 > 1) in a lake. The occurrence of the hydraulic jump was correctly predicted using scaling parameters. It was also shown that topographically induced internal hydraulic jumps form when the nonlinear surges interact with a sill‐contraction topographic feature. In contrast to published literature, the observed high‐frequency nonlinear internal waves were preferentially associated with internal jumps, as opposed to steepened internal surges. Computed vertical diffusivities showed mixing was enhanced by two orders of magnitude within both the surges and hydraulic jumps as they propagated through the basin and interacted with topography. Our results can be generalized to other lakes and fjords with similar long‐narrow geometry and topographically separate side basins.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.196
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 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

Citations28
Published2012
Admission routes1
Has abstractyes

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