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Record W4388781149 · doi:10.1016/j.ecoenv.2023.115676

Micro (nano) plastics uptake, toxicity and detoxification in plants: Challenges and prospects

2023· review· en· W4388781149 on OpenAlexaff
Zahra Dehghanian, Behnam Asgari Lajayer, Zahra Biglari, Shahnoush Nayeri, Mohammad Ahmadabadi, Leila Taghipour, Venkatramanan Senapathi, Tess Astatkie, G.W. Price

Bibliographic record

VenueEcotoxicology and Environmental Safety · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMicroplasticsEnvironmental chemistryDetoxification (alternative medicine)AquaculturePhytotoxicityHuman healthEnvironmental scienceBiologyChemistryFish <Actinopterygii>AgronomyFishery

Abstract

fetched live from OpenAlex

Plastic pollution has emerged as a global challenge affecting ecosystem health and biodiversity conservation. Terrestrial environments exhibit significantly higher plastic concentrations compared to aquatic systems. Micro/nano plastics (MNPs) have the potential to disrupt soil biology, alter soil properties, and influence soil-borne pathogens and roundworms. However, limited research has explored the presence and impact of MNPs on aquaculture systems. MNPs have been found to inhibit plant and seedling growth and affect gene expression, leading to cytogenotoxicity through increased oxygen radical production. The article discusses the potential phytotoxicity process caused by large-scale microplastics, particularly those unable to penetrate cell pores. It also examines the available data, albeit limited, to assess the potential risks to human health through plant uptake.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.243
Teacher spread0.213 · 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

Citations42
Published2023
Admission routes1
Has abstractyes

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