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Record W2121003413 · doi:10.4314/mcd.v5i1.57335

Madagascar rosewood, illegal logging and the tropical timber trade

2010· article· en· W2121003413 on OpenAlexaff
JL Innes

Bibliographic record

VenueMadagascar Conservation & Development · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLoggingIllegal loggingDeforestation (computer science)TropicsCITESAgroforestryBusinessGeographyEndangered speciesGovernment (linguistics)Tropical forestSustainabilityNatural resource economicsForestryEcologyEnvironmental scienceEconomicsBiologyHabitat

Abstract

fetched live from OpenAlex

Although deforestation rates in the tropics are reportedly slowing, the loss of both forest area and forest quality remains a significant issue for many countries. This is particularly true of Madagascar, where recent government instability has enabled a significant increase in the incidence of illegal logging of Dalbergia species from National Parks such as Marojejy and Masoala. The logs are exported with relative ease as export permits are being made available. While attempts have been made to improve the management of tropical forests, in 2005, the International Tropical Timber Organization considered that only 7 % of tropical production forests were being managed sustainably. Given the challenges associated with halting illegal logging at source, emphasis has shifted to the control of the trade in forest products. The Convention on the International Trade in Endangered Species provides a mechanism to restrict such trade, but the Madagascan Dalbergia species are not listed. In the USA, the recent amendments to the ‘Lacey Act’ could provide a significant disincentive to the import of illegally logged wood products, but it remains to be seen whether this Act can be enforced effectively.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.181
Teacher spread0.173 · 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.

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

Citations115
Published2010
Admission routes1
Has abstractyes

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