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Record W2031429490 · doi:10.5539/mas.v8n5p36

The Economic Value of Laem Phak Bia Mangrove Ecosystem Services in Phetchaburi Province, Thailand

2014· article· en· W2031429490 on OpenAlexvenueno aff
Sitthinan Wiwatthanapornchai, Chucheep Piputsitee, Samakkee Boonyawat

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersChaipattana Foundation
KeywordsMangroveTotal economic valueMangrove ecosystemLivelihoodEcosystem servicesGeographyHectareEcosystemAgroforestryForestryAgricultural economicsEnvironmental protectionFisheryEcologyEnvironmental scienceAgricultureEconomicsBiology

Abstract

fetched live from OpenAlex

Thailand is a one of the nation in Southeast Asia, covered by numerous mangrove areas approximately 244,000 hectares. Phetchaburi province is the one of the province in Thailand where the mangrove area has been increasing continually since King’s Royally Initiated Laem Phak Bia Environmental Research and Development Project has been set up. The mangrove ecosystems functions are vital to the livelihood of the surrounding community. Laem Phak Bia community is one that has been served from mangrove ecosystem services. This study assessed the economic value of Laem Phak Bia mangrove ecosystem services in an area approximately 237.44 hectares using Participatory Economic Valuation (PEV) by the villagers ranking and rating the importance of mangrove ecosystem services with a valuable thing for living that is the rice value. The results showed that this mangrove area was worth a total economic value about 100 million Baht per year or 424 thousand Baht per hectares per year. It could be divided into the value of regulation functions, production functions, habitat functions and information functions, which were about 38, 8, 25, and 29 million Baht per year, respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.320

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)0.0000.000

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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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