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Record W2182482836 · doi:10.22230/jem.2007v8n2a512

Big expectations for small forest tenures in British Columbia

2007· article· en· W2182482836 on OpenAlexaffabout
Lisa Ambus, D'Arcy Davis-Case, Stephen Tyler

Bibliographic record

VenueJournal of Ecosystems and Management · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsArbutus Biopharma (Canada)Vancouver Community College
Fundersnot available
KeywordsCertified woodBusinessPopularityForest managementChristian ministryLegislatureSustainable forest managementGeographyForestryAgroforestryEnvironmental resource managementPolitical scienceEconomicsEnvironmental science

Abstract

fetched live from OpenAlex

Small tenures diversify British Columbia?s forest tenure system and create new opportunities for local involvement in forest management. Held by local people and organizations, small tenures generate expectations that forest management will reflect a broad range of community values for forest use. Woodlot Licences and Community Forest Agreements (CFAs) are small, area-based, long-term licences that grant exclusive rights to manage and harvest timber on public land. Community Forest Agreements also include limited rights to botanical non-timber forest products. All tenures, whether large or small, carry rules established by the provincial forestry legislative and regulatory framework. Award and renewal of cfas is conditional upon demonstrating community support and receiving a satisfactory performance evaluation by the British Columbia Ministry of Forests and Range. Small tenures are growing in popularity, but account for only a tiny fraction of the provincial forest land base and allowable annual cut. Despite their size, there are big expectations that woodlots and community forests will introduce new ways of managing forests in British Columbia.

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)0.002
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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

Citations12
Published2007
Admission routes2
Has abstractyes

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