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Record W3206048502 · doi:10.5539/jas.v13n11p118

Economic Viability of Cassava Residues in the Nile Tilapia Diet

2021· article· en· W3206048502 on OpenAlexvenueno aff
Jaomara Nascimento, Niraldo José Ponciano, Marcela Brite Alfaiate, Manuel Vazquez Vidal, Marize Bastos de Matos, Geraldo Pereira, Carla Roberta Ferraz Carvalho Bila, Dálcio Ricardo de Andrade, Geraldo de Amaral Gravina, Roger Figueiredo Daher

Bibliographic record

VenueJournal of Agricultural Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsTilapiaNile tilapiaInternal rate of returnAquacultureContext (archaeology)Agricultural scienceAquaculture of tilapiaBiologyProduction (economics)BiotechnologyFeed conversion ratioAnimal scienceFish <Actinopterygii>OreochromisBusinessFisheryBody weightEconomics

Abstract

fetched live from OpenAlex

Fish feed represents between 50% and 70% of intensive aquaculture production costs. In this context, the present study aimed to evaluate the economic viability of the production of Nile tilapia under a diet with cassava residues in it. To evaluate the economic efficiency in relation to the inclusion of residues in the tilapia feed, the approximate cost of feed per kilogram of live weight (CMR) gained during the experimental period was determined, and later the cash flow of the production was raised for analysis of indicators: NPV (Net Present Value), CBI (Cost Benefit Rate), and IRR (Internal Rate of Return) submitted to different discount rates from existing financing sources in the region. It was found that tilapia under the diet with the inclusion of tapioca sweep in the diet, showed greater economic efficiency, and consequently higher NPV (R $ 4,583.33), IRR (15%) and CBR (1.17). In this sense, the viability analysis showed that cassava residues in diets of tilapia diets, indicate to be a viable strategy to better develop aquaculture production, in a more sustainable way, enhancing the technical and economic viability and minimizing the environmental impacts.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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