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Record W4401366972 · doi:10.1201/9781003323037-87

Scale model study of simple energy dissipation features at low head dams

2024· book-chapter· en· W4401366972 on OpenAlexaboutno aff
Mitchel Provan, A. J. Rayner, Andrew Cornett

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsTailwaterDissipationInflowSimple (philosophy)Head (geology)Flow (mathematics)Scale (ratio)Energy (signal processing)Environmental scienceMarine engineeringComputer scienceCivil engineeringEngineeringGeotechnical engineeringGeologyMechanicsGeography

Abstract

fetched live from OpenAlex

Hydraulic structures across Canada will need to be adapted to deal with Canada’s changing climate. A changing climate may produce more frequent and more extreme flow conditions which can lead to issues with downstream scour. Many energy dissipation methods and devices have been previously studied and implemented; however, these methods are not always suitable for adapting existing infrastructure as they require expensive flow bypassing and dewatering in order to install. There is a need to develop new simple, low-cost energy dissipation methods that can be used to easily adapt existing hydraulic structures to cope with a changing climate. An existing 1:24 physical model of a low head dam was used to test two simple energy dissipation methods; an array of boulders and a set of concrete steps, along with a more elaborate concrete lined pool. The methods were exposed to three inflow discharges at two tailwater depths and their performance were quantified by measuring downstream velocities and scour. Several recommendations for implementing the methods were concluded from the model tests.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.542
Threshold uncertainty score0.966

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 head0.010
GPT teacher head0.233
Teacher spread0.224 · 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 designSimulation or modeling
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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