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Record W4309561851 · doi:10.1111/rec.13835

Soil mounding as a restoration approach of seismic lines in boreal peatlands: implications on microtopography

2022· article· en· W4309561851 on OpenAlexafffundabout
Jaime Pinzón, Anna Dabros, Philip G.K. Hoffman

Bibliographic record

VenueRestoration Ecology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersNatural Resources Canada
KeywordsPeatElevation (ballistics)BorealEnvironmental scienceTaigaTree lineWetlandGeologyHabitatPhysical geographyEcologyClimate changeGeographyPaleontologyBiology

Abstract

fetched live from OpenAlex

Seismic lines—narrow and straight corridors from which overstory has been removed to allow access for oil and gas exploration—are a major human footprint in the boreal forest of western Canada. With slow to minimal recovery of tree cover along these corridors, seismic lines have become a persistent landscape feature affecting connectivity and habitat quality in forested ecosystems, particularly in wetland areas. Soil mounding is a common ground preparation treatment widely applied along seismic lines, with the expectation that it will enhance tree seedling establishment and improve the return of tree cover to disturbed areas. However, much is still unknown about environmental responses following treatment application. In this study, we compared the ground microtopography in treated and untreated seismic lines, as well as the relative elevation between treated and untreated seismic lines with their adjacent treed peatland. The ground elevation in both treated and untreated sites was significantly lower on seismic lines relative to their adjacent treed peatland, with a greater elevation difference in treated areas. Likewise, ground microtopography was orders of magnitude higher along treated areas compared to the natural variation in the adjacent treed peatland. Given the important changes in relative elevation and topography following treatment application, our results suggest the potential for eventual treatment success may be more unpredictable than expected; this may have critical consequences for other ecological properties beyond the restoration goal of tree establishment and return of tree cover.

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 score0.061
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.236 · 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

Citations13
Published2022
Admission routes3
Has abstractyes

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