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Record W1700987130 · doi:10.1093/wjaf/26.1.30

Costs, CO2 Emissions, and Energy Balances of Applying Nordic Slash Recovery Methods in British Columbia

2011· article· en· W1700987130 on OpenAlexaboutno aff
Ola Lindroos, Björn Nilsson, Taraneh Sowlati

Bibliographic record

VenueWestern Journal of Applied Forestry · 2011
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsSlash (logging)Environmental scienceBiomass (ecology)Greenhouse gasPayload (computing)Fuel efficiencyForestryEngineeringGeographyAgronomyComputer scienceEcologyAutomotive engineering

Abstract

fetched live from OpenAlex

Abstract This study evaluated the costs, CO2 emissions, and energy balances associated with three potential systems for recovering roadside slash in British Columbia, Canada, in which the biomass is transported as slash, hog fuel, or bundles. Costs, CO2 emissions, and energy balances of all three systems showed strong dependence on transportation distance and considerably weaker dependence on slash amounts at landing (cutting block size). The results indicated that the hog fuel system is the cheapest, per unit of delivered biomass, whereas the bundle system is the most expensive system when transportation distances are short (<100 km), and the slash system is the most expensive when transportation distances exceed 100 km. However, the viability of the systems is strongly dependent on payload assumptions.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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 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

Citations10
Published2011
Admission routes1
Has abstractyes

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