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Record W2045069008 · doi:10.5539/enrr.v3n1p68

Risk Assessment Analysis of Accelerated Gully Erosion in Ikpoba Okha Local Government Area of Edo State, Nigeria

2012· article· en· W2045069008 on OpenAlexvenueno aff
A. Adediji, Iyamu Felix

Bibliographic record

VenueEnvironment and Natural Resources Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsLocal government areaGeographyErosionLand useHydrology (agriculture)Environmental scienceSocioeconomicsPhysical geographyWater resource managementLocal governmentGeologyArchaeologyGeomorphology

Abstract

fetched live from OpenAlex

The problem of accelerated erosion has been on in Ikpoba-Okha Local Government Area (LGA) in particular and Nigeria at large for some decades now and all past attempts at solving the problem have proved ineffective and thus constituting risk to the people living around the gullies in the area. Therefore, this study aimed at determining sediment loss from the gully sites, the vulnerable areas and threat posed by impact of gullies in the study LGA. Oregbeni Housing Estate and Ede School gully erosion sites in the Ikpoba-Okha LGA were purposively selected for this study. Primary data were collected using GPS receiver.These include the geographic coordinates and elevation of the study gullies catchments which were integrated with the secondary data obtained from satellite image, topographic, geologic, road and lay out maps of the area using Arc GIS 9.3 software. The results of the satellite image classification analysis showed that accelerated gully erosion accounted for 2% (100466.57 m2) of the total areal extent of the study LGA (5189010.57 m2). Of this 2%, Queen Ede School gully accounted for 96957.13 m2. The total estimated sediment loss from Queen Ede School and Oregbeni Housing Estate gullies were 359,173.22 and 48,212.62 tonnes, respectively. These indicated severe land degradation in the study area.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.291
Teacher spread0.273 · 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.

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

Citations4
Published2012
Admission routes1
Has abstractyes

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