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Record W4406842347 · doi:10.5558/tfc2025-001

Forest biomass for bioenergy as a tool to mitigate climate change: Implications for sustainable forest management in eastern Canada

2025· article· en· W4406842347 on OpenAlexaffvenueabout
Claudie‐Maude Canuel, Évelyne Thiffault, Eric R. Labelle, Nelson Thiffault

Bibliographic record

VenueThe Forestry Chronicle · 2025
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsUniversité LavalNatural Resources Canada
Fundersnot available
KeywordsBioenergyClimate changeBiomass (ecology)AgroforestrySustainable forest managementForest managementClimate change mitigationEnvironmental scienceNatural resource economicsEnvironmental resource managementEnvironmental protectionGeographyBusinessRenewable energyEcologyEconomics

Abstract

fetched live from OpenAlex

Bioenergy produced from residual forest biomass can replace fossil fuels, and its contribution is essential to energy transition. However, its supply costs and selling value hardly ensure its profitability, so it has difficulty competing with other energy sources. In addition, concerns persist about the ecological impacts of forest bioenergy, particularly regarding the carbon cycle, biodiversity and site productivity. Our objective is to identify ways of facilitating the development of the forest bioenergy sector within the sustainable forest management framework of eastern Canada. We reviewed the literature to address the role of forest biomass for bioenergy from an operational, silvicultural and ecological perspective. It emerged that forest bioenergy represents an opportunity for the development of the forestry sector. However, the specifications of the forest biomass to be harvested need to be clarified in order to harmonize its mobilization within the existing industrial ecosystem, while maintaining the ecological functions of forest ecosystems. Integrating biomass harvesting with silvicultural and forest management activities is a key element for developing profitable forest bioenergy business plans.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.986

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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 designTheoretical or conceptual
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

Citations6
Published2025
Admission routes3
Has abstractyes

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