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Record W1688067640 · doi:10.1139/l2012-112

Velocity distributions near the inlet of corrugated steel pipe culverts

2012· article· en· W1688067640 on OpenAlexaffvenue
Martin Hunt, Shawn P. Clark, Rob Tkach

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsManitoba HydroUniversity of Manitoba
Fundersnot available
KeywordsCulvertInletFlow velocityGeologyJet (fluid)Acoustic Doppler velocimetryGeotechnical engineeringFlow (mathematics)MechanicsLaser Doppler velocimetryGeomorphologyPhysics

Abstract

fetched live from OpenAlex

This paper presents the findings of a study examining the velocity field within the inlet region of a corrugated steel pipe (CSP) culvert model with vertical headwall, 45° wingwall, and projecting end inlet treatments. Also examined are the effects of embedding the culvert below the stream bed and backfilling the culvert with granular material. Three-dimensional velocity distributions were measured in an effort to better understand how these inlet treatments may affect fish passage. The study examined velocity structure within a CSP culvert with a diameter of 0.8 m at a flow rate of 0.175 m3/s. Measurements were recorded using acoustic Doppler velocimeters at four locations; 0.25, 0.5, 1, and 2 diameters downstream of the inlet. The velocity field of each inlet configuration was dominated by a central jet of high velocity flow surrounded by a low velocity recirculation zone. Analysis of the percent area less than Uavg for each inlet treatment found that the projecting end configuration contained the largest low velocity zone. The usefulness of the low velocity recirculation zone as a fish passage corridor may however be limited by the presence of significant vertical and spanwise velocities as well as high shear zones.

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)0.001
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.179
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 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

Citations12
Published2012
Admission routes2
Has abstractyes

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