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Record W2028223498 · doi:10.2533/chimia.2014.806

Chemical Aspects of Nanoparticle Ecotoxicology

2014· article· en· W2028223498 on OpenAlexaff
Laura Sigg, Yang Yue, Hannah Schug, Lena A. Kosak née Röhder, Flavio Piccapietra, Nikša Odžak, Carl W. Isaacson, Ksenia J. Groh, Renata Behra, Kristin Schirmer

Bibliographic record

VenueCHIMIA International Journal for Chemistry · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsMcGill University
FundersBundesministerium für Bildung und ForschungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsNanoparticleNanotoxicologySilver nanoparticleChemistryEcotoxicityDissolutionEnvironmental chemistryReactive oxygen speciesOrganismCerium oxideNanotechnologyFish <Actinopterygii>EcotoxicologyMaterials scienceOxideBiologyToxicityBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Nanoecotoxicology strives to understand the processes and mechanisms by which engineered nanoparticles (ENP) may exert toxic effects on aquatic organisms. Detailed knowledge of the chemical reactions of nanoparticles in the media and of their interactions with organisms is required to understand these effects. The processes of agglomeration of nanoparticles, of dissolution and release of toxic metal ions, and of production of reactive oxygen species (ROS) are considered in this article. Important questions concern the role of uptake of nanoparticles in various organisms, in contrast to uptake of ions released from nanoparticles and to nanoparticle attachment to organism surfaces. These interactions are illustrated for effects of silver nanoparticles (AgNP), cerium oxide (CeO2 NP) and titanium dioxide (TiO2 NP), on aquatic organisms, including algae, biofilms, fish cells and fish embryos.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.280
Teacher spread0.267 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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