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Record W2539039058 · doi:10.5539/enrr.v6n4p51

Community Opinions about African Wild Dog Conservation and Relocations near the Serengeti National Park, Tanzania

2016· article· en· W2539039058 on OpenAlexvenueno aff
Emmanuel H. Masenga, Richard D. Lyamuya, Mjingo E Eblate, Robert Fyumagwa, Eivin Røskaft

Bibliographic record

VenueEnvironment and Natural Resources Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersTanzania Wildlife Research InstituteNorges Teknisk-Naturvitenskapelige Universitet
KeywordsNational parkTanzaniaTribeGeographyPsychological interventionConservation scienceSocioeconomicsCommunity-based conservationProtected areaEcosystemEcologyEnvironmental planningPsychologySociologyBiologyArchaeologyAnthropology

Abstract

fetched live from OpenAlex

Conservation of the African wild dog (Lycaon pictus) in human-dominated landscapes faces many challenges. Understanding human opinions of wild dog conservation is important to inform management decisions. Questionnaire surveys, including both open and closed-ended questions, were administered by researchers through face-to-face interviews of 297 respondents in the eastern part of the Serengeti ecosystem between January and February 2012. Our results indicated that most local people believed that wild dogs were extinct in the Serengeti ecosystem. According to the local people, wild dogs should have a high conservation priority. Moreover, tribe and gender are important demographic variables that explain the negative or positive perceptions ofattempts to relocate wild dogs from the Loliondo Game Controlled Area to the Serengeti National Park (SNP). We conclude that future conservation interventions should focus on the interface between community knowledge and modern conservation science.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.036
GPT teacher head0.291
Teacher spread0.255 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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