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Record W2025570549 · doi:10.5558/tfc2014-011

Detecting regional differences in within-wildfire burn patterns in western boreal Canada

2014· article· en· W2025570549 on OpenAlexaffvenueabout
David Andison, Kris McCleary

Bibliographic record

VenueThe Forestry Chronicle · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsParks Canada
Fundersnot available
KeywordsBorealFoothillsTaigaFire regimeFire ecologyGeographyEcologyPhysical geographyDisturbance (geology)EcosystemRange (aeronautics)Environmental scienceForestryGeologyCartographyBiologyArchaeology

Abstract

fetched live from OpenAlex

Under the auspices of ecosystem-based management (EBM), using historical range of variation (HRV) knowledge to help guide forest management decision-making is becoming commonplace. In support of this evolution, we hypothesized that historical fire-scale wildfire burn patterns in western boreal Canada could be differentiated by major ecological zones. We tested 10 fine-scale burn pattern metrics for 129 natural wildfires across more than 100 million ha of western boreal Canada against existing Canadian and provincial ecological classification schemes. The results showed some evidence of two historic disturbance regimes. Fires in the Foothills and Rocky Mountain ecoregions tended to have more disturbed patches, a smaller dominant disturbed patch, and less area in partially disturbed island remnants relative to fires in the Boreal Forest and Boreal Shield. However, several key metrics such as event shape and total remnant area were zone-invariant. Fire regime parameters such as fire size and frequency may not be linked to more detailed fire behaviour parameters such as remnant patterns. The moderate, yet highly variable levels of remnant pattern variation we found across the study area represents a natural, and potentially universal source of structural and compositional diversity for the boreal that may be critical to its sustainability.

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 score0.106
Threshold uncertainty score0.427

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 head0.009
GPT teacher head0.198
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

Citations33
Published2014
Admission routes3
Has abstractyes

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