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Record W4401648800 · doi:10.5376/jeb.2024.15.0018

Energy Recovery Applications of Microbial Fuel Cells in Wastewater Treatment

2024· article· en· W4401648800 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Energy Bioscience · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobial fuel cellWastewaterWaste managementEnvironmental scienceEnergy recoveryEnergy (signal processing)Pulp and paper industryProcess engineeringChemistryEnvironmental engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Microbial fuel cell (MFC) is a potential technology that combines pollution reduction and renewable energy generation in wastewater treatment. Microbial fuel cell (MFC) technology, as an innovative solution for achieving pollutant degradation and renewable energy production in wastewater treatment, has received widespread attention in recent years. This study explores the principles, mechanisms, and applications of MFC in wastewater treatment. Through case studies of industrial wastewater, the practical application and energy recovery potential of MFC are demonstrated, and its performance is compared with traditional methods. By optimizing and promoting MFC technology, this study expects to improve the energy efficiency of wastewater treatment, achieve environmental sustainability, and provide policy support recommendations.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.197
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