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Development of production extruded feed for tilapia industrial production

2019· article· en· W2962804209 on OpenAlexaboutno aff
В. Н. Василенко, Л. Н. Фролова, И. В. Драган, N. A. Mihajlova

Bibliographic record

VenueProceedings of the Voronezh State University of Engineering Technologies · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsnot available
Fundersnot available
KeywordsTilapiaCommercial fish feedExtrusionDried fishFeed conversion ratioFish farmingStarchFish <Actinopterygii>Production (economics)AquacultureBiotechnologyFood scienceEnvironmental scienceBusinessPulp and paper industryFisheryChemistryBiologyEngineeringBody weightMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

As a result of the analysis of domestic experience over the past 10 years and foreign experience (on the example of 9 countries: Vietnam, India, Spain, Canada, China, Norway, the USA, Chile, Japan, which possess the most advanced technologies and equipment for the production of highly effective feed for fish ) in the development of innovative technologies and equipment for the production of highly effective feed for fish, it was found out that the methods of both dry and wet pressing are outdated and do not meet modern requirements for the production of feed for fish, as they do not allow to carry out deep physicochemical transformations in the protein-carbohydrate complex and to introduce fat components at 40% level. Modern fish technologies are based on the use of extrusion processing of a multicomponent mixture to impart different buoyancy and adjustable immersion speed of the resulting feed. Extrusion technology will allow to introduce a large amount of fat into the product - up to 35–40%, to achieve 100% starch cleavage level. Extruded product has high water resistance, keeps its shape. New generation developed compound feed formulations for tilapia with 60% protein content, 40% fat, with the introduction of growth stimulants, dietary supplements, etc., will increase the digestibility of compound feed by fish by 10-12%, increase of fish weight by 10-12%, reduce the cost of commercial fish farming by 10–15%, reduce feed conversion by 15%. The proposed technology will allow to create new generation compound feeds formulations for various fish species with a high content of protein-fat complex, which will increase the weight gain of fish by 12-17%, reduce the cost of final fish production by reducing the cost of feed by 10-15%. .

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 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 score0.018
Threshold uncertainty score0.160

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 studies0.0000.000
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 head0.021
GPT teacher head0.164
Teacher spread0.143 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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