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Record W2077467872 · doi:10.1080/19390450903350812

Can Payments for Watershed Services Help Finance Biodiversity Conservation? A Spatial Analysis of Highland Guatemala

2010· article· en· W2077467872 on OpenAlexaboutno aff
Stefano Pagiola, Wei Zhang, Ale Colom

Bibliographic record

VenueJournal of Natural Resources Policy Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersNature Conservancy
KeywordsBiodiversityPaymentEcosystem servicesBiodiversity conservationQuarter (Canadian coin)Measurement of biodiversityWatershedNatural resource economicsBusinessEnvironmental resource managementWork (physics)GeographyEnvironmental planningEcologyEnvironmental scienceEconomicsEcosystemFinanceBiology

Abstract

fetched live from OpenAlex

ABSTRACT Payments for environmental services (PES) are a promising mechanism for conservation. PES could either provide additional funding for protected areas, pay land users to conserve biodiversity outside protected areas, or both. PES require a secure long-term source of financing to work effectively. Obtaining payments directly for biodiversity conservation is difficult, however. In most cases, water users are the most likely source, either directly or indirectly. Thus the potential for PES to help conserve biodiversity depends, in a large measure, on the degree to which areas of interest for conservation of water services overlap with areas of interest for conservation of biodiversity. This paper examines the extent of such an overlap in the case of highland Guatemala. The results show that this potential varies substantially within the country, with some biodiversity conservation priority areas having very good potential for receiving payments, and others little or none. Overall, about a quarter of all biodiversity conservation priority areas have potential for receiving payments. Thus PES are far from being a silver bullet for biodiversity conservation, but they can make a meaningful contribution to this objective.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.308
Teacher spread0.282 · 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

Citations22
Published2010
Admission routes1
Has abstractyes

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