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Record W4391531395 · doi:10.3390/f15020298

Can the Impact of Gravel Roads on Organic Layer Thickness Explain the Distribution of Populus tremuloides along Road Networks in the Boreal Forest of Eastern Canada?

2024· article· en· W4391531395 on OpenAlexafffundabout
Mathilde Marchais, Dominique Arseneault, Yves Bergeron

Bibliographic record

VenueForests · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsUniversité du Québec à RimouskiUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsTaigaEnvironmental scienceBorealDistribution (mathematics)Physical geographySoil scienceGeologyGeographyForestryPaleontologyMathematics

Abstract

fetched live from OpenAlex

Roads are known to alter environmental conditions and the composition of road edge plant communities, particularly when exogenous materials are used as road surfacing. In this study, we evaluate the impact of gravel roads on the organic layer thickness (OLT) and aspen distribution in a boreal forest landscape of Eastern Canada. The OLT and aspen distribution were compared at different distances from the roads (0 m, 10 m, and >10 m) to determine whether a reduction in the OLT along the roads could explain the distribution of aspen along the road network, and in particular the role of the roads as habitat corridors. In addition, germination tests were carried out to determine whether mineral soil from the roads could promote aspen establishment, by comparing the germination rate of substrates consisting only of mineral soil or mosses, and substrates consisting of mosses covered with 0.5 cm or 2 cm of mineral soil. The presence of aspen in the study landscape is limited by thick organic deposits (≥50 cm). However, the thickness of these deposits is reduced to approximately 10 cm at the edges of gravel roads, in part by the transport of mineral soil from the roads. This reduction in the OLT facilitates the establishment of aspen and helps explain its distribution along the road network.

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 score0.125
Threshold uncertainty score0.214

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 head0.012
GPT teacher head0.245
Teacher spread0.234 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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