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Record W2112973163 · doi:10.5539/jsd.v8n3p43

Products Produced from Organic Waste Using Managed Ecosystem Fermentation

2015· article· en· W2112973163 on OpenAlexvenueno aff
Edward A. Calt

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
Fundersnot available
KeywordsBiomass (ecology)FermentationBiodegradable wasteEcosystemChemical industryOrganic matterBusinessPortfolioCelluloseProcess (computing)Environmental scienceBiochemical engineeringPulp and paper industryChemistryBiologyEcologyFood scienceComputer scienceBiochemistryEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

Biomass is the only renewable source of organic chemicals available. The Managed Ecosystem Fermentation process biologically converts organic waste into high value industrial chemicals over night. MEF modifies and regulates a process that has worked in nature for millions of years to extract valuable basic chemicals that are used in industry today, thereby converting the handling of waste from an expense to a source of revenue. MEF is the only known process that can convert cellulose into protein. Unlike most processes that produce a single product, the MEF produces a portfolio of products. MEF produces a portfolio of products ranging from enzymes, proteins and multiple long and short chain fatty acids. The MEF process is based on the microbial ecosystem in a ruminant animal. It is a multispecies process involving over 3,000 species of microbes simultaneously. It works because a multispecies system has more chemical pathways to breakdown the organic matter than a single species.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.209
Teacher spread0.190 · 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

Citations46
Published2015
Admission routes1
Has abstractyes

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