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Record W2082098295 · doi:10.5558/tfc81582-4

The adaptive capacity of forest management to changing fire regimes in the boreal forest of Quebec

2005· article· en· W2082098295 on OpenAlexaffvenueabout
Héloïse Le Goff, Alain Leduc, Yves Bergeron, Mike Flannigan

Bibliographic record

VenueThe Forestry Chronicle · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceNatural Sciences and Engineering Research Council of CanadaUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Montréal
Fundersnot available
KeywordsClimate changeForest managementSustainable forest managementEnvironmental resource managementVulnerability (computing)TaigaAdaptive managementBorealEnvironmental scienceForest restorationFire regimeDisturbance (geology)ZoningAdaptive capacityGeographyForest ecologyAgroforestryEcologyEcosystemForestryEngineeringComputer science

Abstract

fetched live from OpenAlex

Climate influences natural processes at multiple spatial and temporal scales. Consequently, climate change raises many challenges for sustainable forest management; among them, the integration of fire and forest management is increasingly discussed. We propose here an evaluation of the adaptive capacity of forest management under changing forest fire regimes under climate change in the boreal forest of Quebec. Adaptation begins by reinterpreting current practices dealing with climatically driven variability. Among them, fire suppression, and regeneration enhancement can contribute to coping with some impacts of climate change. However, there is an increasing need to develop more integrative and spatially explicit management strategies to decrease the vulnerability of forest management to changing fire risk. Some developing management strategies, such as fuel management or the triad approach (zoning system for conservation, intensive, and extensive forest management), present an interesting potential for integrating the fire risk in management plans. While fuel management and fire suppression are indicated for particularly severe fire regimes, protection against insects, and maintaining a shorter disturbance cycle using forest management represent the preferred adaptation options where the fire cycle is lengthening under climate change. Key words: forest fire, fire risk, climate change, vulnerability, impacts, adaptation strategy, adaptation options, sustainable forest management, fire 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)0.001
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 score0.536
Threshold uncertainty score0.980

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 science0.0010.000
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 head0.010
GPT teacher head0.211
Teacher spread0.202 · 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

Citations34
Published2005
Admission routes3
Has abstractyes

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