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A welfare study into capture fisheries in cirata reservoir: a bio-economic model

2018· article· en· W2970425582 on OpenAlexaboutno aff
Zuzy Anna, Purna Hindayani

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic rentEconomic surplusWelfareQuarter (Canadian coin)Profit (economics)FisheryOverexploitationFisheries managementBusinessMaximum sustainable yieldFood securityEconomicsYield (engineering)Agricultural economicsNatural resource economicsFishingAgricultureEcologyMicroeconomicsMarket economyGeography

Abstract

fetched live from OpenAlex

Capture fishery in inland such as reservoirs can be a source of food security and even the economy and public welfare of the surrounding community. This research was conducted on Cirata reservoir fishery in West Java, to see how far reservoir capture fishery can contribute economically in the form of resource rents. The method used is the bioeconomic model Copes, which can analyze the demand and supply functions to calculate the optimization of stakeholders' welfare in various management regimes. The results showed that the management of capture fishery using Maximum Economic Yield regime (MEY) gave the most efficient result, where fewer inputs would produce maximum profit. In the MEY management, the producer surplus obtained is IDR 2,610.203.099, - per quarter and IDR 273.885.400,- of consumer surplus per quarter. Furthermore, researches showed that sustainable management regime policy MEY result in the government rent/surplus ofIDR 217.891,345, - per quarter with the average price of fish per kg being IDR 13.929. In open access fishery, it was shown that the producer surplus becomesIDR 0. Thus the implementation of the MEY-based instrument policy becomes a necessity for Cirata reservoir capture fishery.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.017
GPT teacher head0.199
Teacher spread0.183 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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