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Record W2524321334 · doi:10.3934/energy.2016.5.697

A life cycle inventory analysis of wood pellets for greenhouse heating: a case study at Macdonald campus of McGill University<sup>1</sup>

2016· article· en· W2524321334 on OpenAlexaffabout
Tingting Wu, Kakali Mukhopadhyay, Paul J. Thomassin

Bibliographic record

VenueAIMS energy · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsMcGill University
Fundersnot available
KeywordsPelletsGreenhouse gasEnvironmental scienceGreenhouseFossil fuelWaste managementLife-cycle assessmentGreen woodBiomass (ecology)BioenergyWood fuelEnvironmental engineeringBiofuelProduction (economics)EngineeringWater contentMaterials scienceHorticultureAgronomyEcologyWood drying

Abstract

fetched live from OpenAlex

Wood pellets are one of the most promising alternatives to fossil fuel in Canada. Using wood pellets for heating allows saving on heating source expenses as compared to fossil fuels. Moreover, direct carbon emissions from wood pellets are regarded as carbon neutral since regrowth of vegetation captures and stores carbon that already exists in the atmosphere. Using wood pellets as a heating fuel for greenhouse vegetable production is expected to result in less greenhouse gas emissions than fossil fuels. Increasing the domestic consumption of wood pellets for greenhouse heating in Canada would reduce the environmental impact of energy consumption. This study investigates the potential of using wood pellets as an alternative fuel for commercial greenhouses in Quebec. This study applied a life-cycle analysis to demonstrate the energy flows and environmental consequences of using wood pellets for greenhouse vegetable production. The results found that greenhouse gas emissions from wood pellets are lower than natural gas in greenhouse operations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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.

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

Citations1
Published2016
Admission routes2
Has abstractyes

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