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Record W2028061135 · doi:10.5558/tfc83347-3

Climate change and protected areas policy, planning and management in Canada's boreal forest

2007· article· en· W2028061135 on OpenAlexafffundvenueabout
Daniel Scott, Christopher J. Lemieux

Bibliographic record

VenueThe Forestry Chronicle · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsClimate changeEnvironmental resource managementProtected areaBorealGeographyForest managementEnvironmental planningEcologyEnvironmental scienceForestry

Abstract

fetched live from OpenAlex

For over a decade, the international scientific community and protected areas professionals have recognized that climate change will have critical implications for protected areas policy, planning and management. However, only a limited literature to date has focused on the implications of climate change for specific protected areas jurisdictions (i.e., national and/or provincial/territorial parks systems). This paper provides an overview of the potential impacts of climate change on Canada's system of boreal protected areas, highlighting the cross-jurisdictional policy, planning and management sensitivities in this biome. Results of a nation-wide climate change survey with protected area organizations are also presented, which reveal a strong incongruity between the perceived salience of climate change for protected area policy and management and a lack of available resources to provide capacity to deal with the challenge of climate change adaptation. To safeguard against the limitations of traditional protected areas system planning, and to ensure the persistence of boreal ecodiversity over the 21 st century and beyond, we call for more rigorous and practical discussion by Canadian protected areas agencies and organizations on the issue of climate change and for a collective and proactive management response. Key words: protected areas, climate change, boreal forest, Canada, adaptation, impacts, policy, planning, management

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 score0.367
Threshold uncertainty score0.395

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 studies0.0000.000
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 head0.028
GPT teacher head0.222
Teacher spread0.194 · 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

Citations20
Published2007
Admission routes4
Has abstractyes

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