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Record W3095921345 · doi:10.1139/cjfr-2020-0314

Fifty years of wildland fire science in Canada

2020· article· en· W3095921345 on OpenAlexafffundvenueabout
Sean C. P. Coogan, Lori D. Daniels, Den Boychuk, Philip J. Burton, Mike Flannigan, Sylvie Gauthier, Victor Kafka, Jane S. Park, B. Mike Wotton

Bibliographic record

VenueCanadian Journal of Forest Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsBanff CentreGovernment of CanadaParks CanadaUniversity of AlbertaNatural Resources CanadaUniversity of Northern British ColumbiaOntario Forest Research InstituteUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFire ecologyWildfire suppressionFire protectionEnvironmental resource managementEnvironmental scienceClimate changeForest managementGeographyForestryEcologyEngineeringEcosystemCivil engineering

Abstract

fetched live from OpenAlex

We celebrate the 50th anniversary of the Canadian Journal of Forest Research by reflecting on the considerable progress accomplished in select areas of Canadian wildland fire science over the past half century. Specifically, we discuss key developments and contributions in the creation of the Canadian Forest Fire Danger Rating System; the relationships between wildland fire and weather, climate, and climate change; fire ecology; operational decision support; and wildland fire management. We also discuss the evolution of wildland fire management in Banff National Park as a case study. We conclude by discussing some possible directions in future Canadian wildland fire research including the further evaluation of fire severity measurements and effects; the efficacy of fuel management treatments; climate change effects and mitigation; further refinement of models pertaining to fire risk analysis, fire behaviour, and fire weather; and the integration of forest management and ecological restoration with wildland fire risk reduction. Throughout the paper, we reference many contributions published in the Canadian Journal of Forest Research, which has been at the forefront of international wildland fire science.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.256
Teacher spread0.233 · 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

Citations98
Published2020
Admission routes4
Has abstractyes

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