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Record W2117093952 · doi:10.3390/f2020505

Preparing for and Responding to Disturbance: Examples from the Forest Sector in Sweden and Canada

2011· article· en· W2117093952 on OpenAlexaffabout
E. Carina H. Keskitalo, Nicole Klenk, Ryan Bullock, Andrea L. Smith, Dawn R. Bazely

Bibliographic record

VenueForests · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsYork UniversityUniversity of SaskatchewanUniversity of British Columbia
FundersSvenska Forskningsrådet Formas
KeywordsPreparednessClimate changeCorporate governanceForest managementFutures studiesEnvironmental resource managementAdaptive capacityBusinessDecentralizationDisturbance (geology)Context (archaeology)Natural resource economicsEnvironmental planningPolitical scienceEconomicsGeographyEcologyFinanceForestry

Abstract

fetched live from OpenAlex

Coping or adaptation following large-scale disturbance may depend on the political system and its preparedness and policy development in relation to risks. Adaptive or foresight planning is necessary in order to account and plan for potential risks that may increase or take place concurrently with climate change. Forests constitute relevant examples of large-scale renewable resource systems that have been directly affected by recent environmental and social changes, and where different levels of management may influence each other. This article views disturbances in the forest sectors of Sweden and Canada, two large forest nations with comparable forestry experiences, in order to elucidate the preparedness and existing responses to multiple potential stresses. The article concludes that the two countries are exposed to stresses that indicate the importance of the governing and institutional system particularly with regard to multi-level systems including federal and EU levels. While economic change largely results in privatization of risk onto individual companies and their economic resources (in Canada coupled with a contestation of institutional systems and equity in these), storm and pest outbreaks in particular challenge institutional capacities at administrative levels, within the context provided by governance and tenure systems.

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 score0.213
Threshold uncertainty score0.244

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 head0.026
GPT teacher head0.236
Teacher spread0.211 · 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
Published2011
Admission routes2
Has abstractyes

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