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Record W2990167120 · doi:10.1002/9781119417637.ch11

Life Cycle Assessment of the Environmental Performance of Thermochemical Processing of Biomass

2019· other· en· W2990167120 on OpenAlexafffund
Eskinder Gemechu, Adetoyese Olajire Oyedun, Edson Norgueira, Amit Kumar

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesUniversity of AlbertaCenovus Energy
KeywordsLife-cycle assessmentScope (computer science)SustainabilityEnvironmental impact assessmentBiomass (ecology)StandardizationEnvironmental scienceEnvironmental resource managementEnvironmental economicsComputer scienceRisk analysis (engineering)BusinessProduction (economics)EcologyEconomics

Abstract

fetched live from OpenAlex

Life cycle assessment (LCA) can be used to evaluate the environmental sustainability and socioeconomic implications of thermochemical processes. This chapter describes the procedure for performing an LCA and reviews the literature on environmental performance of bioenergy systems. It provides an overview of the concept of LCA as an environmental assessment tool and its methodological foundation, and performs an extensive literature review on the use of LCA on thermochemical biomass processing pathways. The chapter also identifies the research gaps and the environmental bottlenecks relevant to the large-scale implementation of different thermochemical processes, and suggests ways to address the gaps. The International Organization for Standardization (ISO) provides the principles and frameworks and requirements and guidelines for the goal and scope definitions, inventory analysis, life cycle impact assessment, and interpretation to have a common ground for individuals, industries, and governments performing an LCA.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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 designSimulation or modeling
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

Citations4
Published2019
Admission routes2
Has abstractyes

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Same topicThermochemical Biomass Conversion ProcessesFrench-language works237,207