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Record W2039581599 · doi:10.1300/j091v21n04_03

Integrating Protected Areas, Plantations, and Certification

2005· article· en· W2039581599 on OpenAlexaboutno aff
Bruce Cabarle, Nick Brown, Kerry Cesareo

Bibliographic record

VenueJournal of Sustainable Forestry · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCertified woodWildlifeForest managementCertificationBusinessEnvironmental resource managementContext (archaeology)AgroforestryWork (physics)GeographyForest ecologySafeguardingScope (computer science)Environmental planningForestryPolitical scienceEcosystemEcologyEngineeringEnvironmental science

Abstract

fetched live from OpenAlex

Abstract The World Wildlife Fund (WWF) has conducted significant work in the areas of forest conservation and sustainable management. The main findings of WWF's Howard and Stead (2001), as outlined in The Forest Industry in the 21st Century report, make the case for meeting the world's forest products needs from one-fifth of the world's forest estate. In addition, WWF's experience in the realm of forest management certification sheds light on certification's potential to ensure conservation benefits from plantations and help overcome some of the present challenges faced by plantation forestry. Recognizing protected area needs (particularly in the US and Canada) on an ecoregional level and helping to define and identify High Conservation Value Forests (HCVFs) are also important components of WWF' s work in forest conservation. Within the context of the plantations and protected areas debate, some of WWF' s research and analysis suggests that the pace and scope in which the projected expansion of fast-growing tree-plantations can be developed within the court of public opinion will be determined largely by commensurate efforts to secure adequate protected areas and the safeguarding of forest found to be of high conservation value. Key Words: World Wildlife Fund (WWF)High Conservation Value Forests (HCVF)certificationecoregions

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.486

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.008
GPT teacher head0.232
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 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

Citations9
Published2005
Admission routes1
Has abstractyes

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