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A Comprehensive Exploration of Biomass Gasification Technologies Advancing United Nations Sustainable Development Goals: Part I

2024· article· en· W4394715504 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJohnson Matthey Technology Review · 2024
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBiomass (ecology)Sustainable developmentBusinessEnvironmental planningEnvironmental scienceWaste managementEnvironmental protectionNatural resource economicsEnvironmental resource managementEngineeringPolitical scienceGeologyEconomicsOceanography

Abstract

fetched live from OpenAlex

The pursuit of sustainable energy sources on a worldwide scale is a crucial and pressing matter, with the United Nations Sustainable Development Goals (UNSDGs) offering a comprehensive framework for properly addressing this challenge. This two-part paper provides an overview of the various technologies now available for the process of biomass gasification. Compared to other renewable energy sources, which have undergone significant technological advancements in recent years, the field of biomass conversion is still relatively new. Keeping up with the newest breakthroughs becomes increasingly crucial as new conversion techniques are rapidly being created. In the thermochemical conversion process called ‘biomass gasification’, biomass solid source materials are degraded or incompletely burned in an oxygen-free or oxygen-deficient high-temperature atmosphere, resulting in the production of biomass gas. Part I delves into different biomass gasification techniques, including upstream, gasification and downstream processes, highlighting their importance in transforming biomass into clean and combustible gases.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.577
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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