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Record W4389246070 · doi:10.17076/eb1803

EXPERIENCE OF USING THE FEED ADDITIVE “FLAVOMYCIN 80” IN GROWING RAINBOW TROUT IN A RECYCLING WATER SYSTEM (RVC)

2023· article· en· W4389246070 on OpenAlexaboutno aff
S. V. Matrosova, Н А Сидорова, Тамара Юрьевна Кучко, I. V. Kamenev, Георгий Дмитриевич Преображенский, Евгения Валерьевна Празднова, Svetlana Matrosova, N. N. Sidorova, Tamara Kuchko, Ivan G. Kamenev, Georgy Preobrazhensky

Bibliographic record

VenueProceedings of the Karelian Research Centre of the Russian Academy of Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsnot available
Fundersnot available
KeywordsRainbow troutBreedFish <Actinopterygii>Animal scienceTroutBiologyFishery

Abstract

fetched live from OpenAlex

The article presents the results of evaluation of the effectiveness of the drug "Flavomycin 80" in feed for rainbow trout Kamloops breed when grown in a plant with a closed water supply system. It is shown that this feed additive leads to an increase in the viability of trout yearlings and an improvement in fish breeding and biological indicators of fish. The use of the drug at a dosage of 44 mg/kg contributed to a decrease in the intensity of damage to trout by pathogens of bacterioses of various etiologies. The positive bacteriostatic effect of the feed additive Flavomycin 80 on the development of conditionally pathogenic intestinal microflora of fish has been proven.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.095
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.108
GPT teacher head0.333
Teacher spread0.225 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Karelian Research Centre of the Russian Academy of SciencesSame topicFood Industry and Aquatic BiologyFrench-language works237,207