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Record W2766600555 · doi:10.15866/irece.v8i5.12862

Assessment of Piano Key Weirs Cost-Effectiveness: a Moroccan Case Study

2017· article· en· W2766600555 on OpenAlexaff
Amal Aboulhassane, Said Rhouzlane, Driss Ouazar, Moulay Hafid Sounny Slitine

Bibliographic record

VenueInternational Review of Civil Engineering (IRECE) · 2017
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsSiltationWeirEnvironmental scienceContext (archaeology)Flood mythHydrology (agriculture)Civil engineeringWater resource managementEngineeringGeotechnical engineeringGeologyGeography

Abstract

fetched live from OpenAlex

In a water scarcity context, dam reservoirs play a key role in the socio-economic development. Currently, these dams face two major problems: the siltation that reduces their storage capacity, and the risk of submersion during the extreme floods events, which is directly related to the hydraulic insufficiency of spillways. To overcome these potential deficits in terms of storage capacity and flood discharge, at the least cost, the Piano Key Weir "PKW" can constitute an advantageous alternative solution. In this paper, an exhaustive methodology to assess the PKW cost-effectiveness with a Moroccan case study are presented. This study is carried out on 32 large Moroccan dams. Results show that the PKW allows increasing the total capacity of the studied reservoirs by 1150 Mm3 that is about 20% of their current capacity, at a total cost of 930 MMAD. This recovered volume exceeds by far the total volume lost through siltation. The best cost-effectiveness is observed in both hydraulic basins of Loukkos and Sebou and particularly within dams of low specific flow rates and moderate hydraulic heads. A PKW cost-effectiveness analysis for increasing the flood discharge capacity and a PKW comparison to other alternative solutions are presented as well.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.324
Teacher spread0.307 · 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
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

Citations3
Published2017
Admission routes1
Has abstractyes

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