MétaCan
Menu
Back to cohort
Record W4313033289 · doi:10.56431/p-5lr918

Toxic Effect of Single Walled Carbon Nanotubes Combined with Cadmium to the Crustacean <i>Daphnia magna</i>

2015· article· en· W4313033289 on OpenAlexaff
Messika Revel, Michel Fournier, Christian Gagnon, Pierre Yves Robidoux

Bibliographic record

VenueInternational Letters of Natural Sciences · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsEnvironment and Climate Change CanadaNational Research Council CanadaUniversité du Québec
Fundersnot available
KeywordsDaphnia magnaCadmiumToxicitySorptionCarbon nanotubeEnvironmental chemistryAdsorptionChemistryAcute toxicityNuclear chemistryToxicologyBiologyMaterials scienceNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

The aim of this study was to assess the impact of single-walled carbon nanotubes (SWCNT) on the toxicity of cadmium (Cd) using the crustacean Daphnia magna . LC50 of Cd and SWCNT alone and combined were calculated and compared. Sorption of Cd on SWCNT was also quantified in separate batch experiments. Results showed that the maximum adsorption of Cd onto SWCNT calculated by the Langmuir equation was 24.4 mg kg-1. LC50s for Cd and SWCNT alone were 252.3 µg L -1 and 1400 µg L -1 , respectively. In the presence of 500 and 1000 µg L -1 of SWCNT, Cd LC50s were 127.2 and 120.1 µg L -1 respectively. Therefore, Cd toxicity increased when organisms were exposed to both contaminants which indicated that SWCNT induces a synergistic toxic effect on the survival of D. magna. It appears that even if SWCNT had a low adsorption capacity for Cd, toxicity of the metal can be increased. Our study shows the complexity of SWCNT toxicity and how the understanding of their interactions with other contaminants is crucial to determine the consequences of their release into the environment.

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 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 score0.020
Threshold uncertainty score0.336

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.244
Teacher spread0.231 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Letters of Natural SciencesSame topicNanoparticles: synthesis and applicationsFrench-language works237,207