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Record W4308804042 · doi:10.1016/j.envint.2022.107626

Micro(nano)plastic toxicity and health effects: Special issue guest editorial

2022· editorial· en· W4308804042 on OpenAlexaff
Tony R. ‎Walker, Lei Wang, Alice A. Horton, Elvis Genbo Xu

Bibliographic record

VenueEnvironment International · 2022
Typeeditorial
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsDalhousie University
FundersNatural Environment Research CouncilSight Research UK
KeywordsNano-NanotechnologyEngineeringMaterials scienceChemical engineering

Abstract

fetched live from OpenAlex

Microplastics (MPs) and nanoplastics (NPs), collectively termed “Micro(nano)plastics [MNPs]” in the special issue, compose the vast majority of plastic contaminants. MPs have become ubiquitous in the global environment (Walker, 2021, Allen et al., 2022) and NPs have also been reported in environmental samples (Cai et al., 2021). MPs have been widely detected in hundreds of animal and plant species (Karbalaei et al., 2019, Litterbase, 2022), including human placentas and blood (Leslie and Depledge, 2020, Prata et al., 2020, Ragusa et al., 2021, Leslie et al., 2022) as MPs are inhaled or consumed via food products and drinking water (Danopoulos et al., 2020, Sequeira et al., 2020, Zhang et al., 2020, Adib et al., 2022). Due to their small sizes, ubiquitous and persistent nature, the potential toxicity and health effects of MNPs have attracted significant attention and spurring rapidly-increasing research efforts (e.g., Guo et al., 2020, Castro-Castellon et al., 2021, Karbalaei et al., 2021, Khoshnamvand et al., 2021, Lahive et al., 2022, Palacio-Cortés et al., 2022).
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\nStudies on laboratory animals have mostly focused on aquatic species and have shown accumulation of MNPs in tissues and organs, causing intestinal injuries, increasing oxidative stress, triggering inflammation, neurotoxicity, and impaired development (Castro-Castellon et al., 2021, Karbalaei et al., 2021, Kukkola et al., 2021, Matthews et al., 2021). However, the actual ecological and human health impacts of MNPs are still largely unknown and few published studies have directly investigated the effects of MNPs on humans (Weber et al., 2022). Evaluating the potential adverse ecological and human health effects of MNPs across levels of biological organization has become highly imperative but challenging due to the high heterogeneity of MNPs, unknown environmental concentrations, debated vector effects for associated chemicals, and co-impact with other environmental stressors, such as climate change and other chemical contaminants (Thornton Hampton et al., 2022). Currently, the concentrations of MNPs in the environment may be low, but their increasing inputs are inevitable based on current and projected plastic production data (Borrelle et al., 2020). Therefore, it has become imperative to evaluate the potential ecological and human health impacts of MNPs.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.002

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 head0.004
GPT teacher head0.218
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations27
Published2022
Admission routes1
Has abstractyes

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