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Record W1946996323 · doi:10.1139/cjce-2012-0429

Experimental investigation of the effect of obstacles on the behavior of turbidity currents

2013· article· en· W1946996323 on OpenAlexvenueno aff
Mohammad Reza Oshaghi, Hossein Afshin, Bahar Firoozabadi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsnot available
FundersSharif University of Technology
KeywordsFroude numberInletCurrent (fluid)ObstacleTurbidityMechanicsTurbidity currentEnvironmental scienceAcoustic Doppler velocimetryParticle (ecology)Hydrology (agriculture)Geotechnical engineeringMeteorologyGeologyFlow (mathematics)Laser Doppler velocimetryPhysicsGeomorphologyOceanographyGeography

Abstract

fetched live from OpenAlex

Turbidity current is produced when a particle-laden fluid flows under lighter ambient fresh fluid. The streaming of particle-laden fluid is called a turbidity current and this kind of current is an important mechanism for sediment transportation in lakes and oceans. In the present research, the main concentration is on the effect of obstacle with an isosceles right triangular cross section on the behavior of turbidity current. A series of laboratory experiments were carried out with various obstacle heights and different inlet densimetric Froude numbers. In each experiment, velocity profiles upstream and downstream of the obstacle were measured, using an acoustic Doppler velocimeter. Kaolin was used as the suspended material. Experiments showed that the density current with lower inlet Froude number, reacts to the presence of the obstacle more rapidly compared to the currents with higher values of inlet Froude number. At the upstream section of the obstacle, an increase in the height of the obstacle resulted in a reduction in the current inertia and the maximum velocity. Also the local Froude numbers had an abrupt increase when the dense layer passed the obstacle.

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 score0.072
Threshold uncertainty score0.767

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)0.0010.000

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 head0.013
GPT teacher head0.185
Teacher spread0.172 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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