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Record W2059995368 · doi:10.5558/tfc81088-1

One strategy at work for more than 260 reasons

2005· article· en· W2059995368 on OpenAlexvenueaboutno aff
Jeff Young

Bibliographic record

VenueThe Forestry Chronicle · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Sustainable forest managementNational forestBusinessCertified woodConventionCommunity forestryForest managementEnvironmental resource managementSustainable developmentPolitical scienceForestryGeographyEconomicsEngineering

Abstract

fetched live from OpenAlex

Canadians are taking action to advance sustainable forest management with the National Forest Strategy (NFS) 2003–2008, A Sustainable Forest: The Canadian Commitment. This national policy framework reaffirms Canadians' long-term vision and defines strategic targets to be achieved by the forest community at large. Seen as an international model, the NFS serves to establish partnerships and to promote policies as encouraged by the Final Statement issued at the XII World Forestry Congress. Canada and the United States of America have national forest programmes that are advancing a sustainable North American forest and are contributing to the well-being of the global forest. Stronger mechanisms and liaisons between developed countries with such programmes will help Canada and other likeminded countries to push sustainable forest management concepts even further as they evolve. Key words: global forest convention, international network, national forest programme, National Forest Strategy Coalition, sector roundtable

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)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.304
Threshold uncertainty score0.604

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0170.010
Scholarly communication0.0180.007
Open science0.0020.006
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0400.011

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 head0.021
GPT teacher head0.258
Teacher spread0.237 · 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 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
Published2005
Admission routes2
Has abstractyes

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