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Record W4388775408 · doi:10.3390/toxics11110935

(Micro)Plastics Are Toxic Pollutants

2023· review· en· W4388775408 on OpenAlexaff
Judith S. Weis, Juan José Alava

Bibliographic record

VenueToxics · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicroplasticsPollutantBioaccumulationEnvironmental chemistryEnvironmental scienceBiotaPollutionHazardous wasteEnvironmental protectionChemistryBiologyEcology

Abstract

fetched live from OpenAlex

Plastics, including microplastics, have generally been regarded as harmful to organisms because of their physical characteristics. There has recently been a call to understand and regard them as persistent, bioaccumulative, and toxic. This review elaborates on the reasons that microplastics in particular should be considered as "toxic pollutants". This view is supported by research demonstrating that they contain toxic chemicals within their structure and also adsorb additional chemicals, including polychlorinated biphenyls (PCBs), pesticides, metals, and polycyclic aromatic hydrocarbons (PAHs), from the environment. Furthermore, these chemicals can be released into tissues of animals that consume microplastics and can be responsible for the harmful effects observed on biological processes such as development, physiology, gene expression, and behavior. Leachates, weathering, and biofilm play important roles in the interactions between microplastics and biota. Global policy efforts by the United Nations Environmental Assembly via the international legally binding treaty to address global plastic pollution should consider the designation of harmful plastics (e.g., microplastics) with associated hazardous chemicals as toxic pollutants.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.054
GPT teacher head0.292
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations55
Published2023
Admission routes1
Has abstractyes

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