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Record W2030640161 · doi:10.1139/x04-023

Fire history in relation to site type and vegetation in Vienansalo wilderness in eastern Fennoscandia, Russia

2004· article· en· W2030640161 on OpenAlexvenueno aff
Tuomo Wallenius, Timo Kuuluvainen, Ilkka Vanha‐Majamaa

Bibliographic record

VenueCanadian Journal of Forest Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersAcademy of Finland
KeywordsVegetation (pathology)Fire regimeBorealTaigaPhysical geographyFire historyVegetation typeFire ecologyGeographyCharcoalDisturbance (geology)SwampWilderness areaForestryDendrochronologyEnvironmental scienceEcologyWildernessGeologyArchaeologyGrasslandEcosystemClimate changeGeomorphology

Abstract

fetched live from OpenAlex

A wildfire area in a boreal forest landscape dominated by Pinus sylvestris L., in the Vienansalo wilderness area in eastern Fennoscandia, was examined for its spatial characteristics and fire history. The boundaries of the 360-ha fire that occurred in 1969 were mapped, and the vegetation types of burnt and unburnt areas were inventoried. Fire history was investigated in 40 study plots, and fire scars, tree ages, and charcoal in peat or soil were used for evidence of past fires. The complex shape of the 1969 fire and the detected small-scale variation in past fire frequencies were concordant with the existing small-scale variation in site moisture and vegetation characteristics in the area. Moist depressions, swamps, and more fertile forest patches dominated by Picea abies (L.) Karst. often did not burn when the nearby dryish forest type did. There was also temporal variability in fire frequency. An abrupt increase in the number of fires occurred in the late 17th century. In the mid-19th century, both the number of fires and the annually burnt area in the region decreased. Our results show that in the examined wildfire area, there has been considerable and consistent small-scale spatial variation in fire frequency and that historical fire regime evidently has been strongly affected by human activity.

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)0.000
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 score0.711
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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

Citations95
Published2004
Admission routes1
Has abstractyes

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