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Record W3113048611 · doi:10.5539/jas.v13n1p111

Aqua-Feed Wastes: Impact on Natural Systems and Practical Mitigations—A Review

2020· article· en· W3113048611 on OpenAlexvenueno aff
Jonathan Munguti, James G. Kirimi, Kevin Obiero, Erick Ogello, Domitila Kyule, David Liti, Levi M. Musalia

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsAquacultureEnvironmental scienceEutrophicationWater qualityNutrientBusinessFish <Actinopterygii>FisheryEcologyBiology

Abstract

fetched live from OpenAlex

Dietary composition of aquaculture feeds (aquafeeds) determines the quality of wastes from aquaculture production systems. These wastes, which are derived mainly from nitrogenous and phosphorus compounds subsequently affect water quality in the culture systems and the ambient environment. Depending on the type of culture systems and management practices employed, the aquafeed wastes can influence the water pH, algal turbidity, biological oxygen demand (BOD) and may cause fish mortality. The aquafeed wastes also can facilitate eutrophication leading into harmful algal blooms. Moreover, large quantities of aqua-waste are discharged as fish cannot retain all the food they consume which means a significant portion of the feed remains uneaten. In this paper, we review and discuss practical nutritional strategies and mitigation measures to reduce aquafeed wastes including controlled formulation using high-quality ingredients, enzyme-based aquafeed, processing, reduction of anti-nutrition factors and precision feeding. The paper further recommends strategies for enhancing the resilience of aquaculture production systems and mitigation measures to reduce the effects of aqua-wastes on ambient natural environments.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.962
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.285
Teacher spread0.257 · 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

Citations31
Published2020
Admission routes1
Has abstractyes

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