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Record W3182129610 · doi:10.4236/jgis.2021.134021

GIS Application and Architectural Design for the Assessment of Urban Infrastructural Renovation: Case of the Nsam Market in the Yaounde III Municipality, Cameroon

2021· article· en· W3182129610 on OpenAlexaboutno aff
Elvis Kah, Tang Somo Alain

Bibliographic record

VenueJournal of Geographic Information System · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)RevenueInvestment (military)Plan (archaeology)BusinessMarket researchPopulationFinanceEconomic growthGeographyMarketingEconomicsPolitical science

Abstract

fetched live from OpenAlex

Markets, whose creation needs heavy investment, are seen as centres for economic and financial transactions. To this effect, they play an important role in the survival of city dwellers as well as the embellishment of the city. In 2006, Nsam market was created in the Nsam quarter Yaounde with little financial concentration. Over the years, this market has outlived its usefulness to the extent that its present functioning is an eyesore. Amongst the causes to this are the facts that, the market was created following no predefined standards, the city has grown and the market can no longer handle the dependent population. From field observations, this study aimed at proposing a complete renovated plan for the Nsam market in Yaounde, Cameroon. The study relied on secondary and primary sources of data collected and treated following some pre-set standards. These data enabled an analysis of the diagnosis of the situation prevailing there where it led to the conclusion of a complete renovation of the market. This renovated market is expected to better the working conditions of traders, create more jobs which will boost revenue collection and embellishment of the city amongst others.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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