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Record W4413266944 · doi:10.5539/ijc.v18n1p1

Mass, Energy, and Electron-based Metrics in Anaerobic Digestion

2025· article· en· W4413266944 on OpenAlexvenueno aff
Pong Kau Yuen, Cheng Man Diana Lau, Karen Yuen

Bibliographic record

VenueInternational Journal of Chemistry · 2025
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryBiodegradationMetric (unit)ElectronAnaerobic digestionBiogasOrganic matterEnergy conversion efficiencyProcess engineeringBiochemical engineeringBiological systemThermodynamicsMethaneOrganic chemistryWaste managementPhysicsNuclear physics

Abstract

fetched live from OpenAlex

be represented by Buswell’s equation. When the chemical formula of organic matter is identified, the mean oxidation number of organic carbons, theoretical amount of biomethane, theoretical biomethane potential, and theoretical number of transferred electrons can be determined. Currently, the biodegradability performance of organic matters in anaerobic digestion is measured by two metrics: the biodegradability index and the energy conversion efficiency. However, the concept of electron conversion efficiency has not been rigorously studied. This article serves two purposes: to develop a new electron-based metric, and to investigate the relationships between this metric and the two preexisting biodegradability performance metrics. Having calculated these said metrics through a series of procedures using mass percentages of elements and experimental biomethane potential as key parameters, this research concludes that the microscopic electron conversion efficiency and the macroscopic mass-based biodegradability index are numerically identical, and the electron conversion efficiency and energy conversion efficiency display a strong linear correlation.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.280
Teacher spread0.275 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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