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Record W2167377986 · doi:10.5539/jas.v5n8p132

Assessing Environmental Damage to Marine Protected Area: A Case of Perhentian Marine Park in Malaysia

2013· article· en· W2167377986 on OpenAlexvenueno aff
Gazi Md. Nurul Islam, Kusairi Mohd Noh, Tai Shzee Yew, Aswani Farhana Mohd Noh

Bibliographic record

VenueJournal of Agricultural Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
FundersUniversiti Putra Malaysia
KeywordsMarine protected areaCoral reefOverfishingMarine ecosystemCoral reef protectionFishingMarine habitatsMarine conservationMarine reserveMarine spatial planningHabitatGeographyFisheryTourismEnvironmental resource managementEcosystemEcologyEnvironmental scienceEnvironmental planningBiology

Abstract

fetched live from OpenAlex

The Perhentian Island located in the East coast of Peninsular Malaysia is well-known for its rich coral reef ecosystems. Marine resources of Malaysia have been overexploited due to overfishing and tourism activities. As such no-take marine protected area (MPAs) were established in Malaysia, including Perhentian Island Marine Park to enable overexploited marine resources to recover and to conserve coral reef ecosystems. This paper examines the current level of activities causing damage to coral reef habitats in the Perhentian MPA. This study used paired comparison method to elicit the perception of local stakeholders on activities harmful to the marine habitats. The results of the analysis showed that various respondent groups had similar preference rankings on the harmful activities: littering, discarding fishing equipment, excess fishing and too many divers that cause damage to habitats in the MPA area. The findings suggest that policy makers should take cognizance of the local stakeholders’ concern in planning and designing of marine protected areas in Malaysia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.208
Teacher spread0.201 · 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.

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

Citations21
Published2013
Admission routes1
Has abstractyes

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