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Record W1903582008 · doi:10.14796/jwmm.r236-18

Low-Flow Modification of Flood Control Channels in Cities

2010· article· en· W1903582008 on OpenAlexafffundvenue
Celia Fan, James Li, Grace K. Luk, Onyx W. H. Wai

Bibliographic record

VenueJournal of Water Management Modeling · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaHong Kong Polytechnic University
KeywordsFlood controlFlow (mathematics)Environmental scienceFlood mythControl (management)Hydrology (agriculture)GeographyGeologyComputer scienceGeotechnical engineeringMechanicsArchaeologyPhysics

Abstract

fetched live from OpenAlex

Many rivers and streams throughout the world have been severely affected by human activities in the past century including water abstraction, watershed land use changes, power generation, and dam and levee construction. In cities, engineering practices advocate straightening, enlarging, and converting the natural rivers and streams into concrete channels to maximize the efficiency of conveying the floodwaters away from populated areas. These engineering structures disrupt the natural equilibrium of fluvial systems and eliminate macroinvertebrates and aquatic and riparian species in watercourses. With a growing awareness of the value of the natural riverine ecosystems, a global movement of river management has shifted from manipulation and control to restoration and conservation in the past decade The objective of this research is to develop a general stream restoration approach for providing low flow habitat in flood control channels in highly urbanized areas. The restoration goals are: (i) to provide a natural and selfsustainable river and geomorphic system; (ii) to establish appropriate pools and riffles and in-stream covers for extending and providing a better living conditions of the aquatic habitats; and (iii) to maintain flood control function. A concrete flood channel in Yuen Long, Hong Kong, that drains into a nature reserve, the Mai Po Nature Reserve in Deep Bay, will be used as a pilot site to demonstrate the design methodology and application of the flood channel restoration approach. Meanders, deflectors and instream covers will be applied to the

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)0.001
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 score0.138
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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

Citations0
Published2010
Admission routes3
Has abstractyes

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