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Record W2089783181 · doi:10.1080/03601230903404341

<i>In vitro</i>effects of pesticides and metals on the activity of acetylcholinesterase (AChE) from different tissues of the blue mussel,<i>Mytilus trossulus</i>L.

2009· article· en· W2089783181 on OpenAlexfundno aff
Justyna Kopecka-Pilarczyk

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsnot available
FundersUniversity of Saskatchewan
KeywordsCarbarylDichlorvosAcetylcholinesteraseCarbofuranMytilusPesticideAchéChemistryChlorpyrifosEnvironmental chemistryToxicologyMusselGillBlue musselOrganophosphateBiologyBiochemistryFish <Actinopterygii>EnzymeEcologyFishery

Abstract

fetched live from OpenAlex

The objective of this study was to conduct a comprehensive comparison of the effect on acetylcholinesterase (AChE) from various tissues of Mytilus trossulus caused by in vitro exposure to several pesticides and metals, because results available in the literature are inconsistent, difficult to compare, and sometimes contradict each other. For this purpose, fraction S10 extracted from gills, digestive gland, mantle and muscles, and the whole soft tissue of the mussel was exposed to several pesticides (dichlorvos, chlorpyrifos, fenitrothion, carbofuran and carbaryl) and metals (Cu, Zn, Cd, Hg, Pb) at a wide range of concentrations. AChE was inhibited in 50% or more in all the tissues exposed to dichlorvos, Cu, Hg and the mixture of Cu+Cd, and in some tissues exposed to carbaryl and carbofuran. The IC(50) was calculated where possible. No inhibition was found in the case of chlorpyrifos, Cd, Pb, and Zn.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.744

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 studies0.0000.002
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 head0.017
GPT teacher head0.281
Teacher spread0.265 · 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

Citations11
Published2009
Admission routes1
Has abstractyes

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Same venueJournal of Environmental Science and Health Part BSame topicEnvironmental Toxicology and EcotoxicologyFrench-language works237,207