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Record W2964543512 · doi:10.18331/sfs2019.6.1.9

Impacts of copper sulfate on hematological parameters of rainbow trout (Oncorhynchus mykiss, Walbaum, 1792)

2019· article· en· W2964543512 on OpenAlexvenueno aff
Damir Suljević

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsRainbow troutFisheryCopper sulfateCopperSulfateZoologyBiologyChemistryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Copper sulfate has been used for many years as an algaecide and parasite treatment. The usage of copper has certain issues as there is a thin line that separates effective treatment levels from overdoses. Copper sulfate can be extremely toxic to fish under certain conditions. This study is focused on the effects of copper sulfate on some blood indices in Oncorhynchus mykiss during a 24 hour period. Our research used one procedure 40 Oncorhynchus mykiss with the average weight of 220 ±10 g. The experimental group had 20 fish that were treated with the blue vitriol (dose of 0,012 g/40l), during a 24 hour period. The control group had 20 fish. The presence of copper in the water leads to the significant increase of erythrocyte, hemoglobin, MCHC and leukocyte levels while the MCV levels were noted to be considerably low. Monocytes, unsegmented and segmented granylocites were significantly increased in the experimental group of fish. Lymphocyte count was considerably reduced in the same group of fish.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.109
GPT teacher head0.262
Teacher spread0.154 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2019
Admission routes1
Has abstractno

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