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Record W4249571487 · doi:10.20431/2349-0381.0807001

Climate Change and the Economic Vulnerability of Household in Niger Delta Region

2021· article· en· W4249571487 on OpenAlexfundno aff
Job Imharobere Eronmhonsele, Igbinosa Norris

Bibliographic record

VenueInternational Journal of Humanities Social Sciences and Education · 2021
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
FundersMinistry of EnvironmentInternational Development Research Centre
KeywordsNiger deltaVulnerability (computing)Climate changeDeltaGeographyWater resource managementNatural resource economicsEnvironmental scienceClimatologyEnvironmental planningEnvironmental protectionDevelopment economicsEconomicsGeologyOceanographyComputer scienceEngineeringComputer security

Abstract

fetched live from OpenAlex

Recently there has been a flurry of activities regarding climate change in the Niger Delta region of Nigeria. Some of these have been by non-governmental organizations (NGOs) and civil society groups while others are more academic and policy-oriented. In terms of research, it has been estimated that over 70 million cubic meters daily, amounting to about 70 million tonnes of carbon dioxide are flared off during oil and gas exploration and production activities in the Niger Delta region (UNDP/World Bank 2004). Approximately 75 percent of total gas production in Nigeria is flared. It has been further estimated that Nigeria accounts for about 17.2% of global gas flaring. As a result, more gas is flared in Nigeria's Niger Delta than anywhere in the world. Flaring in Nigeria contributes a measurable percentage of the world's total emissions of greenhouse gases (GHGs) and is probably the greatest contributor of GHGs in the Niger Delta region. Due to the low efficiency of many of the flares much of the gas is released as methane (which has a high warming potential), rather than carbon dioxide. At the same time, the low-lying Niger Delta is particularly vulnerable to the potential effects of sea levels rising.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.527

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 studies0.0000.001
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 head0.092
GPT teacher head0.346
Teacher spread0.254 · 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 designTheoretical or conceptual
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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