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Record W4390056080 · doi:10.20517/wecn.2023.60

Simple detection of polystyrene nanoparticles and effects in freshwater mussels: method development and <i>in situ</i> application to urban pollution

2023· article· en· W4390056080 on OpenAlexafffund
François Gagné, Maxime Gauthier, C. André

Bibliographic record

VenueWater Emerging Contaminants & Nanoplastics · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsContaminationEnvironmental chemistryMusselPollutionSalting outChemistryEnvironmental scienceChromatographyBiologyFisheryAqueous solutionEcology

Abstract

fetched live from OpenAlex

The ubiquity of plastics in environments worldwide is raising concerns about their toxicity to organisms. The purpose of this study was to investigate simple means to determine the exposure and effects of nanoplastics (NPs) in the freshwater mussels Elliptio complanata (E. complanata ). NP tissue levels were determined using a plasmonic nanogold sensor probe and effects were determined using the refractive index (RI) and thiol-reaction rates (TRR) in protein-dense tissue extracts. This method was adapted to quantitatively measure the concentration of NPs in tissues using a salting-out extraction in the presence of acetonitrile (ACN). Concentrated solutions of albumin were first spiked with NPs to evaluate changes in RI and TRR to determine crowding effects. The data revealed that NPs readily decreased the RI and TRR in albumin in vitro . These three simple assays were then applied on freshwater mussels caged for 3 months at various sites in a largely populated area. Mussels downstream of the city center and found at the street runoff discharge sites were highly contaminated by NPs and the RI and TRR were also reduced. In conclusion, simple and readily accessible assays to assess the NP contamination based on a visual nanogold sensor technology, and the effects of plastics are proposed for freshwater mussels.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.599

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)0.0000.000

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 head0.006
GPT teacher head0.222
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes2
Has abstractyes

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