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Record W2009654186 · doi:10.1139/x07-145

Restoration of natural legacies of fire in European boreal forests: an experimental approach to the effects on wood-decaying fungi

2008· article· en· W2009654186 on OpenAlexvenueno aff
Kaisa Junninen, Jari Kouki, Pertti Renvall

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersMaj ja Tor Nesslingin SäätiöMinistry of Environment
KeywordsTaigaThreatened speciesLoggingEcologyBorealPrescribed burnEnvironmental scienceFire regimeBiologyEcosystemHabitat

Abstract

fetched live from OpenAlex

Effective fire suspension in Fennoscandian boreal forests has caused a number of species to become threatened. To compensate for the negative ecological impacts of fire elimination, prescribed burning of forests as a restoration method has been introduced recently. We studied the effects of controlled burning on assemblages of wood-decaying polypores (Basidiomycota), including red-listed species, in a large-scale field experiment in Finland. A total of 24 forest sites were included in the factorial study design with two factors: logging and burning. The presence of polypore fruiting bodies was documented 1 year before the treatments, and 1 and 4 years after the treatments. Over 11 000 observations of 104 species of polypores were made. Change in the fungal species composition due to logging and burning was clear after 4 years. At the species level, the responses to logging and fire varied depending on the species. Treatments increased fruiting of pioneer decayers; however, most red-listed species seemed to suffer. Thus, prescribed burning does not offer immediate benefits for most red-listed species. In unlogged forests, the restorative effects of fire are likely to be seen later as the death and decay processes of trees continue and provide more resources for polypores.

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 score0.153
Threshold uncertainty score0.998

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 science0.0000.000
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 head0.063
GPT teacher head0.273
Teacher spread0.209 · 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

Citations61
Published2008
Admission routes1
Has abstractyes

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