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Record W2041276898 · doi:10.1139/x06-289

The demographic structure of European aspen (Populus tremula) populations in managed and old-growth boreal forests in eastern Finland

2007· article· en· W2041276898 on OpenAlexvenueno aff
Tarja Latva-Karjanmaa, Reijo Penttilä, Juha Siitonen

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTaigaBiodiversitySalicaceaeSilvicultureThinningOld-growth forestEcologyAgroforestryRegeneration (biology)Stand developmentClearcuttingForestryWoody plantBiologyGeography

Abstract

fetched live from OpenAlex

European aspen ( Populus tremula L.) is a keystone species for biodiversity in boreal forests. However, large aspen have largely been removed from managed forests, whereas regeneration and the long-term persistence of mature trees in protected areas are matters of concern. We recorded the numbers of mature (≥20 cm diameter) aspen in old-growth and managed forests in eastern Finland, based on a large-scale inventory (11 400 ha, 36 000 living and dead trees). In addition, saplings and small aspen trees were surveyed on thirty-six 1 ha sample plots. The average volumes of mature living and dead aspen were 4.0 and 1.3 m 3 /ha in continuous old-growth forests and 0.2 and 0.6 m 3 /ha in managed forests, respectively. These results indicate that large aspen trees in managed forests are a legacy of the past, when forest landscapes were less intensively managed. We conclude that the long-term persistence of aspen in protected areas can only be secured by means of restoration measures that create gaps large enough for regeneration to occur. More emphasis should be given to sparing aspen during thinning and to retaining mature aspen during regeneration cutting in managed forests.

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 score0.951
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.056
GPT teacher head0.277
Teacher spread0.222 · 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

Citations60
Published2007
Admission routes1
Has abstractyes

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