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Record W4391343146 · doi:10.1080/02827581.2024.2305186

Temporal changes in forest floor carbon stocks following scarification in boreal lichen woodlands

2024· article· en· W4391343146 on OpenAlexafffundabout
Boris Dufour, François Hébert, Jean‐François Boucher

Bibliographic record

VenueScandinavian Journal of Forest Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec à ChicoutimiMinistère des Ressources naturelles et des Forêts (Québec)Cégep de Baie-ComeauUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScarificationMicrositeForest floorEnvironmental scienceAfforestationTaigaForestryCoarse woody debrisWoodlandBorealAgroforestryEcologyGeographySoil scienceAgronomyBiologySeedlingSoil waterHabitat

Abstract

fetched live from OpenAlex

Uncertainties remain regarding the carbon (C) loss due to scarification in afforested lichen woodlands (LW), which originate from regeneration failures of closed-crown black spruce- feathermoss stands due to compounded disturbances. Therefore, the objective of this study was to characterize the C stock changes in the forest floor of scarified, unharvested LWs. Ten afforestation trials were established from 1999 to 2014 in LWs in the managed boreal forest of Québec. Ground surface layers were sampled in 2017 for different microsites. From 3 to 18 years after treatment, scarified floors exhibited ≈ 2 Mg ha−1 C loss, due to opposite trends in the furrow and ridge microsites. Both gradually approached the undisturbed C density level of forest floor between furrow pairs and between skidder trails microsites without reaching it after 18 years. This suggests that microsite C density continued to evolve afterward, and that losses due to scarification might be recovered, due to a higher potential gain in the furrow microsites combined with a lower expected loss in the ridge microsites. Carbon managers should use a permanent 2 Mg ha−1 C loss in the forest floor due to scarification in LWs, acknowledging that this is offset by the growth of planted trees.

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 score0.068
Threshold uncertainty score0.947

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 integrity0.0000.001
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 head0.053
GPT teacher head0.309
Teacher spread0.256 · 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
Published2024
Admission routes3
Has abstractyes

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