MétaCan
Menu
Back to cohort
Record W4284977873 · doi:10.21203/rs.3.rs-1789667/v1

First report on the toxicity of copper and zinc on the Afrotropical whirligig beetle, Orectogyrus alluaudi (Coleoptera: Gyrinidae): Effect on survival and oxidative stress

2022· preprint· en· W4284977873 on OpenAlexaff
Babatunde O. Amusan, Ibukunoluwa Balogun, Ayorinde Fola Koleosho, Hamzat O. Fajana, Olugbenga J. Owojori

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsOxidative stressZoologyBiologyCopperToxicologyChemistryEndocrinology

Abstract

fetched live from OpenAlex

Abstract Freshwater whirligig beetles are important macroinvertebrates that help maintain freshwater quality by removing dead insects from the surfaces of lakes, ponds, or streams. In this study, we investigated the response of the whirligig beetle, Orectogyrus alluaudi to copper (Cu) and zinc (Zn) by assessing their survival and biochemical responses after 7 days of the whirligig beetle exposure to the metals. Copper significantly reduced the survival of the beetles with LC50 (median lethal concentration) of 223–100 mg L − 1 at 24 to 168 hours of exposure. However, the LC50 of Zn was only significant at 72 h (482 mg L − 1 ) to 168 h (150 mg L − 1 ). Copper, at the lowest exposure concentration of 15 mg L − 1 , induced oxidative stress on the beetles by significantly increasing the level of malondialdehyde [MDA], a biomarker of lipid peroxidation. There was also significant glutathione [GSH] reduction at the low Cu concentration. However, Zn had no significant effect on the oxidative stress response of the beetles. This study showed that O . alluaudi is less sensitive to copper and zinc via dermal exposure routes than other aquatic invertebrates. Therefore, an alternative way of exposure via the ingestion of contaminated food might be an important exposure pathway to assess the toxicity of metals on the beetles.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.320
Teacher spread0.264 · 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 designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueResearch SquareSame topicInsect Pest Control StrategiesFrench-language works237,207