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Record W2074288136 · doi:10.5558/tfc87071-1

The potential of forest biomass as an energy supply for Canada

2011· article· en· W2074288136 on OpenAlexaffvenueabout
David Paré, Pierre Y. Bernier, Évelyne Thiffault, Brian Titus

Bibliographic record

VenueThe Forestry Chronicle · 2011
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsCanadian Sport Centre PacificCanadian Forest ServiceNatural Resources Canada
Fundersnot available
KeywordsGreenhouse gasBiomass (ecology)Environmental scienceAgroforestryEnergy supplyBioenergyScarcityRenewable energyEnvironmental protectionNatural resource economicsEnergy (signal processing)AgronomyEcologyEconomics

Abstract

fetched live from OpenAlex

There is a growing interest in using forest biomass as an energy source. The main objectives of this paper are to give somefigures and perspectives on Canadian forest biomass supply with respect to Canada’s energy demand and to examine thepotential of using this feedstock for reducing our greenhouse gas (GHG) emissions. Published estimates of forest biomasssupply as harvest residues are reported and discussed. The range of estimates listed here indicates that this source ofenergy is important but that it is still a fraction of our energy demands. The potential of using this biomass to reduce ourGHG emissions is strongly dependent, among other factors, on the technological pathways that are used, with direct heatproduction and combined heat and power (CHP) ranking amongst the best options available. The relative scarcity of theresource behooves us to use it efficiently. Key words: forest biomass, residue, greenhouse gas, mitigation, energy, sustainable forestry

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.009
GPT teacher head0.192
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations56
Published2011
Admission routes3
Has abstractyes

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