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Record W4413292965 · doi:10.3390/f16081333

Early Succession Across Boreal Forest Transitions After Linear Disturbance and Wildfire

2025· article· en· W4413292965 on OpenAlexafffundabout
Colleen M. Sutheimer, Scott E. Nielsen

Bibliographic record

VenueForests · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEcological successionDisturbance (geology)TaigaBorealEcologyEnvironmental scienceGeographySecondary successionStand developmentPhysical geographyForestryAgroforestryGeologyBiologyGeomorphology

Abstract

fetched live from OpenAlex

Anthropogenic disturbances interact with wildfire, altering successional dynamics across North America’s boreal forest. Linear disturbances, including seismic lines used for oil and gas exploration, dissect forests, while wildfire is a fundamental agent of forest succession. However, little is known about early succession dynamics after both seismic line creation and wildfire, especially across transitions from uplands to peatlands. To address this, we characterized and compared regeneration and recruitment after individual and successive disturbances in peatland, transitional, and mesic upland forests across the oil sands region of Alberta, Canada. We used non-metric multidimensional scaling to compare composition and mixed-effects generalized linear models to compare densities of trees and tall shrubs 10 to 24 years after disturbance. Compositionally, regeneration was similar within forest types and between transitional and peatland forests, while patterns in recruitment were more influenced by past disturbances. Overall, we found evidence of dominant, additive, and interactive effects on early successional patterns within linear disturbances in boreal forests. In transitional and peatland forests, disturbances influenced tree and tall shrub regeneration and recruitment in complex ways. Early successional dynamics after disturbance influence forest structure and composition and are vital to understanding recovery in boreal forests, especially across boreal forest transitions.

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 score0.032
Threshold uncertainty score0.983

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 communication0.0000.000
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 head0.003
GPT teacher head0.232
Teacher spread0.229 · 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

Citations2
Published2025
Admission routes3
Has abstractyes

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