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Record W1530751477 · doi:10.1111/jfr3.12179

Flood risk management in the coastal city of Lagos, Nigeria

2015· article· en· W1530751477 on OpenAlexfundno aff
Ibidun Adelekan

Bibliographic record

VenueJournal of Flood Risk Management · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaInternational Development Research Centre
KeywordsFlood mythMegacityFlood risk managementFlooding (psychology)Context (archaeology)Environmental planningRisk managementGeographyPopulationEnvironmental resource managementWater resource managementBusinessEnvironmental scienceEnvironmental healthFinance

Abstract

fetched live from OpenAlex

Abstract An increasingly important threat to the high population and large concentration of residential, industrial, commercial and urban infrastructure systems in Africa's coastal megacity of Lagos is flooding. Over the past decade, flooding in Lagos has increased significantly, drawing increasing attention to the need for flood risk management. The paper examines the current approach to flood risk management by public and private agents in the city in the context of international flood risk management practices. The implications of existing flood management for future flood risks are also discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations107
Published2015
Admission routes1
Has abstractyes

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