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Record W2094207316 · doi:10.5558/tfc78837-6

Managing timber and non-timber forest product resources in Canada's forests: Needs for integration and research

2002· article· en· W2094207316 on OpenAlexaffvenueabout
Luc Duchesne, Suzanne Wetzel

Bibliographic record

VenueThe Forestry Chronicle · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsForest productBusinessSustainabilityAgricultureResource (disambiguation)Product (mathematics)Forest industryForest managementLoggingNatural resource economicsEnvironmental resource managementAgroforestryForestryGeographyEcologyEconomics

Abstract

fetched live from OpenAlex

Non-timber forest products (NTFP) are emerging globally as a tool for the establishment of sustainable forest communities. They provide employment to various sectors of society, draw on local expertise and culture, and increase the outputs of forests. In recent years, NTFP have received accrued interest by the general public, governments and the private sectors of Canada. However, for the NTFP industry to enter mainstream Canadian industrial culture it is now critical to attempt the integration of the timber industry with the NTFP industry to benefit both sectors. NTFP can be harvested from four types of environment: wild stocks from timber-productive forests, wild stocks from non-timber-productive forests or lands, managed stocks from intensively managed forests, and domesticated stocks from agricultural systems. A large body of evidence suggests that NTFP management and harvest can serve the forest industry in many ways. There are four possible types of interaction between the NTFP and timber industries: independent resource use, competition for resources, complementary resource use and symbiotic resource use. Integration of both industries in a sustainable manner will need to be supported with research that address economic, social, policy and ecological questions. Key words: NTFP, sustainability, biodiversity, community forestry

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.528

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.256
Teacher spread0.236 · 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

Citations19
Published2002
Admission routes3
Has abstractyes

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