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Identification of raw materials that can be used in fish farming in feed manufacturing industries in Senegal

2025· article· en· W4413973289 on OpenAlexfundno aff
Saliou Wade, Abdoulaye Loum, Guillaume Koussovi, P. Diop, Farokh Niass

Bibliographic record

VenueInternational Journal of Fisheries and Aquatic Studies · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsFish <Actinopterygii>Identification (biology)Raw materialAgricultureFish farmingFisheryAquacultureEnvironmental scienceBiotechnologyBusinessBiologyEcology

Abstract

fetched live from OpenAlex

This study is part of a process aimed at enhancing the value of local raw materials used in animal feed manufacturing companies in Senegal and likely to be used in fish feed in aquaculture. The study was carried out in four (4) geographical areas of Senegal (North, South, Centre-West, East), specifically among thirteen (13) active companies. The results of the survey show that 100% of the companies source certain raw materials on the local market. However, the percentage of imported raw materials represents 30.8% of the inputs used in the food manufacturing process. Sixty-one point five percent (61.5%) of companies obtain their raw materials at less than 500 FCFA/kg. The purchase prices of these food inputs and the food production of the companies vary respectively on average between 501 and 750 FCFA/kg, and between 40 and 50 tonnes/year. This food production is intended for livestock, poultry and farmed 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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.262
Teacher spread0.230 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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