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Record W1955175158 · doi:10.5539/enrr.v5n3p98

Wild Fauna Conservation in Nigeria

2015· article· en· W1955175158 on OpenAlexvenueno aff
Mobolaji A. Idowu, Olajumoke A. Morenikeji

Bibliographic record

VenueEnvironment and Natural Resources Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsPoachingWildlifeEndangered speciesEcotourismWildlife conservationNorth American Model of Wildlife ConservationGeographyCITESEnforcementNatural resourceFaunaEnvironmental planningHabitatEnvironmental protectionEnvironmental resource managementTourismEcologyBiologyArchaeology

Abstract

fetched live from OpenAlex

This paper is on the trend and pattern of wild fauna conservation in Nigeria. The trend reveals the successes that have been achieved as well as the challenges that have emerged over the years. Large scale degradation of wild animal natural habitats coupled with continuous overhunting and poaching have resulted in more species becoming endangered in recent times in Nigeria according to recent surveys by WCS (Wildlife Conservation Society, Nigeria). The National Parks Service being the highest conservation agency in the country becomes the major fortress for conserving these endangered species, alongside collaborative efforts from non-governmental conservation agencies like NCF (Nigerian Conservation Foundation) and others. This review paper became necessary to re-emphasise the need for more awareness on the risks involved if the unabated exploitation of our wild fauna resources continues. The paper concludes by offering suggestions on how the conservation agenda can continue in the country through existing Parks and Zoos development, through ecotourism with more media awareness, basic wildlife education in schools, improved research and a better enforcement of existing wild animals’ protection laws.

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 score0.082
Threshold uncertainty score0.666

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.036
GPT teacher head0.288
Teacher spread0.252 · 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
Published2015
Admission routes1
Has abstractyes

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