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Record W2326233168 · doi:10.5558/tfc2012-099

Community forest organizations and adaptation to climate change in British Columbia

2012· article· en· W2326233168 on OpenAlexaffvenueabout
Ella Furness, Harry W. Nelson

Bibliographic record

VenueThe Forestry Chronicle · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClimate changeForest managementEnvironmental resource managementAdaptation (eye)Climate change adaptationVariety (cybernetics)BusinessEnvironmental planningGeographyForestryEcologyEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

The effects of climate change in many regions are expected to be significant, and likely to have a detrimental effect on the health of forests and the communities that often depend on those forests. At the same time climate change presents a challenge as it requires changes in both forest management, and the institutions and policies developed that govern forest management. In this paper, we report on a study assessing how Community Forests Organizations (CFOs) in British Columbia (BC), which were developed to manage forests according to the needs and desires of local communities and First Nations, are approaching climate change and whether or not they are responding to, or preparing for, its impacts. There are practical steps that CFOs can take to improve their ability to cope with future conditions such as planting a wider variety of species, practising different silvicultural techniques and increasing monitoring and observation of the forest. This paper gives an overview of what current capabilities exist in CFOs and suggests potential areas for targeted development.

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 score0.455
Threshold uncertainty score0.880

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 studies0.0010.000
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 head0.025
GPT teacher head0.214
Teacher spread0.189 · 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

Citations15
Published2012
Admission routes3
Has abstractyes

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