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Record W4389943859 · doi:10.1002/exp.20230018

Catalytic and biocatalytic degradation of microplastics

2023· article· en· W4389943859 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueExploration · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaGlobal Water FuturesCanada First Research Excellence Fund
KeywordsMicroplasticsContext (archaeology)Environmental scienceDegradation (telecommunications)PollutantBiochemical engineeringPollutionEnvironmental chemistryChemistryEcologyComputer scienceBiologyEngineering

Abstract

fetched live from OpenAlex

In recent years, there has been a surge in annual plastic production, which has contributed to growing environmental challenges, particularly in the form of microplastics. Effective management of plastic and microplastic waste has become a critical concern, necessitating innovative strategies to address its impact on ecosystems and human health. In this context, catalytic degradation of microplastics emerges as a pivotal approach that holds significant promise for mitigating the persistent effects of plastic pollution. In this article, we critically explored the current state of catalytic degradation of microplastics and discussed the definition of degradation, characterization methods for degradation products, and the criteria for standard sample preparation. Moreover, the significance and effectiveness of various catalytic entities, including enzymes, transition metal ions (for the Fenton reaction), nanozymes, and microorganisms are summarized. Finally, a few key issues and future perspectives regarding the catalytic degradation of microplastics are proposed.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.220
Teacher spread0.199 · 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