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Record W2914378441 · doi:10.2298/abs181220004r

Influence of trout farm effluents on selected oxidative stress biomarkers in larvae of Ecdyonurus venosus (Ephemeroptera, Heptageniidae)

2019· article· en· W2914378441 on OpenAlexaff
Andjelina Radojevic, Dejan Mirčić, Miroslav Živić, Vesna Perić‐Mataruga, Milenka Božanić, Katarina Stojanović, Jovana Lukičić, Ivana Živić

Bibliographic record

VenueArchives of Biological Sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsTroutGlutathione peroxidaseSuperoxide dismutaseOxidative stressEnvironmental chemistryBiologyAnimal scienceChemistryEcologyFisheryFish <Actinopterygii>Biochemistry

Abstract

fetched live from OpenAlex

The aim of the present study was to establish the extent to which the outlet waters of trout farms affect the ecosystems of the Crnica and Skrapez rivers in Serbia. We monitored selected biomarkers of oxidative stress: superoxide dismutase (SOD), glutathione peroxidase (GPx) and total glutathione (GSH) in larvae of the species Ecdyonurus venosus, and simultaneously analyzed the changes in the physical and chemical parameters. The investigations were carried out in spring at four localities along the Skrapez and Crnica rivers: one upstream (the control localities), and three downstream from the fish farm outlets. On the Skrapez River, the fish farm was clearly visible and was markedly changed by the chemical parameters of the water, manifested as a decreased concentration of dissolved oxygen, increased concentrations of nitrates, nitrites, ammonium ions, total phosphorus and total organic carbon, and increased SOD and GPx activities and decreased GSH concentration in larvae from the first downstream locality as compared to the control locality. On the Crnica River, due to the high values of water flow (around 3 m3/s), effluents from the fish farm had no effect on the chemical parameters of the water or on the tested biomarkers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score1.000

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 studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.241
Teacher spread0.229 · 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.

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

Citations6
Published2019
Admission routes1
Has abstractyes

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