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Record W4404869336 · doi:10.1016/j.mec.2024.e00254

From plastic waste to bioprocesses: Using ethylene glycol from polyethylene terephthalate biodegradation to fuel Escherichia coli metabolism and produce value-added compounds

2024· article· en· W4404869336 on OpenAlex
Alexandra Balola, Sofia Ferreira, Isabel Rocha

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.

fundA Canadian funder is recorded on the work.
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

VenueMetabolic Engineering Communications · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaCumming School of Medicine, University of Calgary
KeywordsBiodegradationEthylene glycolPolyethylene terephthalateEscherichia coliPolyethylene glycolEthyleneChemistryPEG ratioPolyethyleneWaste managementMaterials scienceOrganic chemistryBiochemistryBusinessEngineeringCatalysis

Abstract

fetched live from OpenAlex

metabolic and protein engineering strategies. Consequently, we assess the potential of EG as a versatile alternative to conventional carbon sources like glucose, facilitating the closure of the loop between the highly available PET waste and the production of valuable biochemicals. This review explores the interplay between PET biodegradation and EG metabolism, as well as the key challenges and opportunities, while offering perspectives and suggestions for propelling advancements in microbial EG assimilation for circular economy applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.222
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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