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Record W4397005921 · doi:10.1051/alr/2024004

Fostering international and trans-boundary cooperation in the management of Lake Chad fisheries, wildlife and flora: the role of a trans-boundary Ramsar conservation area

2024· article· en· W4397005921 on OpenAlexaff
Prince Emeka Ndimele, Adeniran Akanni, Kehinde Moyosola Ositimehin, Jamiu Adebayo Shittu, Akinloye Emmanuel Ojewole, Yeside Zainab Ayeni

Bibliographic record

VenueAquatic Living Resources · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsBalsillie School of International Affairs
Fundersnot available
KeywordsRamsar siteBoundary (topology)WildlifeWildlife conservationFlora (microbiology)FisheryWildlife managementNature ConservationGeographyFisheries managementEnvironmental resource managementEnvironmental planningEnvironmental protectionEcologyEnvironmental scienceWetlandBiologyFishing

Abstract

fetched live from OpenAlex

The Lake Chad basin is one of the most politico-ecologically complex regions in Africa. The rapid global climate change caused by decades of unsustainable resource utilization has not only impaired the ecosystem function but has escalated further conflict with the associated terrorism in the region. This paper reviews the notion of environmental peacebuilding through the introduction of trans-boundary conservation as a mechanism to achieve peace and harmony in the Lake Chad region. The proposed trans-boundary conservation area will restore ecosystem services, conserve biodiversity, improve livelihood, and reduce poverty in the Lake Chad basin. The paper provides justification for the establishment of the “Lake Chad trans-boundary Ramsar site” as an example of how a trans-boundary conservation area could act as a catalyst for improved political cooperation using inter-linkage with other Multilateral Environmental Agreements in the region.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.004
Scholarly communication0.0040.002
Open science0.0000.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.255
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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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