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Record W4386499819 · doi:10.52267/ijaser.2023.4403

TOWARDS A 3D WEB TOOL FOR VISUALIZATION AND SIMULATION OF URBAN FLOODING: THE CASE OF METROPOLITAN CITIES IN CAMEROON

2023· article· en· W4386499819 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Applied Science and Engineering Review · 2023
Typearticle
Languageen
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsUniversité LavalCentre de Géomatique du Québec
Fundersnot available
KeywordsMetropolitan areaVisualizationFlooding (psychology)Environmental planningComputer scienceGeographyEnvironmental scienceData miningArchaeologyPsychology

Abstract

fetched live from OpenAlex

Today, 3D geo visualization of flood data is perceived as a more realistic and detailed solution for making decisions regarding flood mitigation and adaptation measures.In this paper, after a multi-criteria comparative study of four virtual globes used in the visualization of geospatial flood data, it is found that CesiumJS stands out the most from the other solutions, with a score close to 100% on all criteria grouped in 4 categories (Visualization, Interaction, Quality of support and Experiences).Using CesiumJS and other libraries, we proposed a 3D web solution to dynamically simulate and visualize floods in urban areas of Cameroon.The main objective of this tool is to strongly involve water experts, policymakers and the general public in flood management.Without considering a precise 3D city model, this tool, however, represents a good compromise between the quality of flood management and the cost of better Flood Management by an expert.

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.

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 score0.087
Threshold uncertainty score0.202

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 head0.019
GPT teacher head0.302
Teacher spread0.283 · 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