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Record W2611875625 · doi:10.5539/res.v9n2p222

The Management Model of Fishery Environment in Bengkalis District, Riau Province

2017· article· en· W2611875625 on OpenAlexvenueno aff
Pareng Rengi, Marnis, Fitri Fitri

Bibliographic record

VenueReview of European Studies · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and Coastal Ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityStakeholderSituatedBusinessFisheryEnvironmental resource managementEnvironmental scienceEconomicsManagementEcologyComputer science

Abstract

fetched live from OpenAlex

Research purposes are to design management of fisheries environment sustainable in Bengkalis district. This study was conducted in Bengkalis district, in Riau province. This location is situated in a strategic area of the Malacca Strait.This research used survey method. Types of data collected in the form of primary and secondary data. Data analysis techniques used in this research is descriptive analysis, sustainability analysis (Rapfish), stakeholder needs analysis and prospective analysis.The analysis showed the management of fisheries environmental in Bengkalis district is in the bad category level or less sustainable with MDS value of 39.59 overall. Implementation of management strategies to fisheries environmental in Bengkalis district (P) with the interaction scenario between the migration distance (j), Pressure mangrove land (m), level of education relative coastal communities (r), fisheryenvironmental policy (k), cooperation among stakeholders (s).

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.330

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.031
GPT teacher head0.251
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 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

Citations5
Published2017
Admission routes1
Has abstractyes

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