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Record W2508254053 · doi:10.3390/f7090187

Vegetation Mortality within Natural Wildfire Events in the Western Canadian Boreal Forest: What Burns and Why?

2016· article· en· W2508254053 on OpenAlexaffabout
Colin Ferster, Bianca N.I. Eskelson, David Andison, Valerie LeMay

Bibliographic record

VenueForests · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsParks CanadaUniversity of British Columbia
Fundersnot available
KeywordsVegetation (pathology)TaigaBorealDisturbance (geology)GeographyEnvironmental sciencePhysical geographyFire regimeNatural hazardFire ecologyWindthrowEcologyForestryEcosystemMeteorologyGeology

Abstract

fetched live from OpenAlex

Wildfires are a common disturbance event in the Canadian boreal forest. Within event boundaries, the level of vegetation mortality varies greatly. Understanding where surviving vegetation occurs within fire events and how this relates to pre-fire vegetation, topography, and fire weather can inform forest management decisions. We used pre-fire forest inventory data, digital elevation maps, and records of fire weather for 37 naturally-occurring wildfires (1961 to 1982; 30 to 5500 ha) covering a wide range of conditions in the western Canadian boreal forest to investigate these relationships using multinomial logistic models. Overall, vegetation mortality related to a combination of factors representing different spatial scales. Lower vegetation mortality occurred where there was lower fuel continuity and when fires occurred under non-drought conditions. Higher classification accuracy occurred for class extremes of no mortality (i.e., unburned areas within the burn event) and high mortality; partial vegetation mortality classes were harder to distinguish. This research contributes to the knowledge required for natural pattern emulation strategies, and developing responses to climate change.

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 score0.688
Threshold uncertainty score0.701

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 communication0.0000.001
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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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

Citations21
Published2016
Admission routes2
Has abstractyes

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