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Record W2032950190 · doi:10.1071/wf10049

Spatial pattern analyses of post-fire residual stands in the black spruce boreal forest of western Quebec

2010· article· en· W2032950190 on OpenAlexafffundabout
Amar Madoui, Alain Leduc, Sylvie Gauthier, Yves Bergeron

Bibliographic record

VenueInternational Journal of Wildland Fire · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsNatural Resources CanadaUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Montréal
FundersCanadian Forest ServiceNatural Resources CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesUniversité du Québec à MontréalCanadian Natural Resources Limited
KeywordsBorealBlack spruceTaigaWoodlandWetlandPhysiographic provinceHabitatFire regimePhysical geographyPeatGeographyMossEnvironmental scienceLandformFire ecologyForestryEcologyEcosystemGeologyArchaeologyCartographyGeomorphology

Abstract

fetched live from OpenAlex

In this study, we characterised the composition and configuration of post-fire residual habitats belonging to two physiographic zones of the black spruce–moss domain in western Quebec. Thirty-three large fires (2000–52 000 ha) were selected and extracted on classified Landsat satellite imagery. The results show that a minimum of 2% and a maximum of 22% of burned areas escaped fire, with an overall average of 10.4%. The many forest patches that partially or entirely escaped fire formed residual habitats (RHs). It was found that although the area of RHs follows a linear relationship with fire size, their proportion appears relatively constant. Spatial analyses showed that the fires could be separated into two groups depending on the physiographic zones (East-Canadian Shield v. West-Clay Belt Lowlands). Fires in the west zone generate less RHs and appear to be associated with more extreme weather conditions. In most cases there was no association with water or wetlands; in some fires the presence of RHs is associated with the proximity of water bodies. The failure to find an association between RHs and wetlands suggests that this type of environment is part of the fuel. Coniferous woodland with moss appears particularly overrepresented within RHs. Our results suggest that the local and regional physiographic conditions strongly influence the creation of RHs; therefore, it is important to consider those differences when applying ecosystem-based 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 score0.095
Threshold uncertainty score0.935

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 head0.270
Teacher spread0.259 · 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

Citations55
Published2010
Admission routes3
Has abstractyes

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