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Record W6962754976 · doi:10.17605/osf.io/tcbhk

Evidence in economic valuation of coastal zone and marine ecosystem services

2018· article· en· W6962754976 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystem servicesMultidisciplinary approachEcosystem valuationClimate changeGeneral partnershipValuation (finance)Coastal zoneEcosystem

Abstract

fetched live from OpenAlex

The aim of this review is identify and describe the global evidence regarding the economic valuations of ecosystem services in coastal and marine zones. The economic discipline has developed a range of methods to estimate the values of ecosystem services; however, this is still a challenge. In addition, the use of this information by practitioners is little, while the benefits in the design of public polices to face the pollution risks, conservation and climate change adaptation are open. Here we intend to address these gaps of information by conducting a systematic review. We will address the next question: What evidence exist on economic valuation of ecosystems services provide by coastal zones? The interest in this question is framing in the project “Basin sea interactions with communities – BASIC.” The BASIC Project`s is an applied research on the interactions between basins, sea and communities and focused on the generation of tools for adaptation to climate change for the integrated management of water resources in the coastal zone of Cartagena. This three-year multidisciplinary project carried out with the aid of the International Development Research Center of Canada (IDRC-IDRC). The EAFIT University, in partnership with the University of Los Andes, the University of Cartagena, the FHEO Foundation and the CARDIQUE Regional Corporation, directs the implementation .

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0000.009
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2720.232

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 head0.018
GPT teacher head0.236
Teacher spread0.217 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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