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Record W4386321388 · doi:10.5376/ija.2023.13.0006

Indoor Aquaculture Potential of Duckweed (<i>Lemna minor</i>) and the Need for Adoption in Kenya

2023· article· en· W4386321388 on OpenAlexvenueno aff
Mercy Chepkirui, Paul Orina, Judith Kemunto Achoki, Tonny Orina, Vivian Kemunto, Rebby Jemutai, Jared Ochingo

Bibliographic record

VenueInternational Journal of Aquaculture · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsnot available
Fundersnot available
KeywordsLemna minorAquacultureMacrophyteManureAnimal scienceAquatic plantFish <Actinopterygii>BiologyFisheryAgronomyEcology

Abstract

fetched live from OpenAlex

Duckweed ( Lemna minor ) culture has been explored as a possible macrophyte based ingredient for partial or full substitution of both soy bean or fish meal in fish feeds in China and India but has seldom been explored in Kenya to establish its growth conditions. To demonstrate the culture potential of L. minor &nbsp;in aquaculture, culture technique was homogenized in indoor plastic tanks using chicken manure at an optimum water depth of 30 cm. Temperature ranged from (25.84&plusmn;0.19)&nbsp;to (28.24&plusmn;0.08)&nbsp;&deg;C. After 10 days L. minor &nbsp;attained 100% cover and was harvested three times in a month. The overall yield was (1.09&plusmn;0.09)&nbsp;kg/m 3 /month. The Relative Growth Rates was higher during the first harvest (0.38 g/day) and decreased in subsequently (0.12 and 0.10 g/day) for the second and third harvests respectively. Dissolved oxygen decreased with L. minor &nbsp;growth and ranged between (0.84&plusmn;0.19)&nbsp;and (2.52&plusmn;0.52)&nbsp;mg/L. pH. values ranged between (5.89&plusmn;0.32)&nbsp;and (7.23&plusmn;0.23)&nbsp;throughout the study period. The present study demonstrates that L. minor can be cultured using cheap and locally available organic manure in aquaculture sector. Therefore, there is need to embrace L. minor &nbsp;culture technologies by small scale farmers for sustainable aquaculture production in Kenya.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.325

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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
Published2023
Admission routes1
Has abstractyes

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