MétaCan
Menu
Back to cohort
Record W2990535285 · doi:10.3968/11293

Water Resources and Sub-Saharan African Economy: Anthropogenic Climate Change, Wastewater, and Sustainable Development in Nigeria

2019· article· en· W2990535285 on OpenAlexaffvenue

Bibliographic record

VenueCanadian social science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSustenanceSustainable developmentAgricultureNatural resource economicsCompetition (biology)Food securityDevelopment economicsClimate changeBusinessEconomic growthEconomicsGeographyPolitical scienceEcology

Abstract

fetched live from OpenAlex

Studies have shown that there exists serious competition for freshwater for domestic uses and for the sustenance of agricultural activities in Nigeria. Based on this competition, this study seeks to interrogate how wastewater could be harnessed for development of agriculture in Nigeria. In view of the country’s dwindling economy amidst the current escalating herders-farmers’ conflict over resources for their respective economic activities, water security perspective contributes to our understanding of the roles that resource management can play in economic crisis than other commonly cited factors like income inequality, poor land use policies, ethnicity, and political instability. This paper argues that with Africa’s epileptic economy and increasing population in this climate change era, attainment of some Sustainable Development Goals (SDGs) by 2030 is unrealistic. The study submits that there is the need for a paradigm shift in wastewater perception in Africa, south of the Sahara as this would reduce pressure on freshwater and fast-track sustainable development through increase in agricultural production.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.980

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCanadian social scienceSame topicWater-Energy-Food Nexus StudiesFrench-language works237,207