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Record W2161394832 · doi:10.3109/17435390.2013.778345

Mechanistic insights into the effect of nanoparticles on zebrafish hatch

2013· article· en· W2161394832 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNanotoxicology · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsNational Institute for NanotechnologyNational Research Council CanadaMount Allison UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsZebrafishDanioHatchingZincCadmiumNanoparticleCarbon nanotubeToxicityBiophysicsCell biologyEmbryoBiologyNanotechnologyMaterials scienceChemistryBiochemistryEcologyMetallurgy

Abstract

fetched live from OpenAlex

Aquatic organisms are susceptible to waterborne nanoparticles (NP) and there is only limited understanding of the mechanisms by which these emerging contaminants may affect biological processes. This study used silicon (nSi), cadmium selenide (nCdSe), silver (nAg) and zinc NPs (nZnO) as well as single-walled carbon nanotubes (SWCNT) to assess NP effects on zebrafish (Danio rerio) hatch. Exposure of 10 mg/L nAg and nCdSe delayed zebrafish hatch and 100 mg/L of nCdSe as well as 10 and 100 mg/L of uncoated nZnO completely inhibited hatch and the embryos died within the chorion. Both the morphology and the movement of the embryos were not affected, and it was determined that the main mechanism of hatch inhibition by NPs is likely through the interaction of NPs with the zebrafish hatching enzyme. Furthermore, it was concluded that the observed effects arose from the NPs themselves and not their dissolved metal components.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score0.999

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)0.0010.002

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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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