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Record W2172417503 · doi:10.5558/tfc2012-052

Challenges in the implementation of conservation policies in the Reventazón Model Forest, Costa Rica

2012· article· en· W2172417503 on OpenAlexvenueno aff
José Alberto Cubero Moya, Ligia Quirós, Mildred Jiménez

Bibliographic record

VenueThe Forestry Chronicle · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsReforestationEcosystem servicesSustainabilityLimitingForest coverPayment for ecosystem servicesForest managementLegislationPaymentBusinessNature ConservationForest ecologyEnvironmental resource managementForestryForest restorationEcoforestryEnvironmental protectionEnvironmental planningAgroforestryGeographyEcosystemEnvironmental scienceEcologyPolitical scienceEngineeringFinance

Abstract

fetched live from OpenAlex

We present an analysis of the implementation of the main conservation policies in the Reventazón Model Forest in Costa Rica, and its contribution to the sustainability of environmental services. The existing environmental legislation has helped to curtail environmental degradation and loss of forest cover. The Reventazón Model Forest is an initiative created as a means of implementing Costa Rica’s commitment to the application of the ecosystem approach, and has served as a framework for putting into practice sustainable development policies with broad participation by communities in Cartago Province. The Payment for Environmental Services (PES) program has played a role in forest conservation and reforestation in 9% of the Model Forest. The challenge now is to increase the impact of this program in the Reventazón Model Forest, above all in the biological corridors, and we analyze some of the causes that could be limiting the PES program.

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.648

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0020.001
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 head0.058
GPT teacher head0.282
Teacher spread0.224 · 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 designQualitative
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

Citations1
Published2012
Admission routes1
Has abstractyes

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