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Record W4401648796 · doi:10.5376/ijms.2024.14.0025

Sustainable Management of Northeastern Madagascar Halieutic Resources

2024· article· en· W4401648796 on OpenAlexvenueno aff
Henri Joël Jao, Landy Amelie Soambola, Roger Roger, Tsihoboto Marcellin

Bibliographic record

VenueInternational Journal of Marine Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersNanjing University of Information Science and Technology
KeywordsEnvironmental resource managementGeographyEnvironmental planningBusinessEnvironmental science

Abstract

fetched live from OpenAlex

This present study analyses the management of fishery resources to ensure their sustainability and rational exploitation in fishing villages of the Loky-Manambato marine protected area (LM-MPA), which are located in Vohemar district and SAVA region of the northeastern coast of Madagascar. Based on local surveys and research in rural villages of LM-MPA, this research reveals a strong potential for fishery resources in this area. The artisanal fishing plays a vital role as main source of income for the surrounding fishermen community rural area village residents and contributes to poverty reduction. The number of fishermen is increasing and fishing techniques continue to evolve over time in the LM-MPA villages. Different types of fishery resources, fishing gear and catching methods are exploited within the LM-MPA. However, the practice of new, destructive fishing techniques is accentuating the reduction in fishery products and threatening the local mangrove environment. It is therefore important to educate fishermen to stop using this detrimental fishing technique to ensure the sustainability of fishery resource. The sustainable management and closure as solution to ensure an increase in fishermen’s catch yields and to protect the environmental resource are investigated. Effective improvement measures are recommended.

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 score0.194
Threshold uncertainty score0.912

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.229
Teacher spread0.221 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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