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Record W4412927409 · doi:10.5376/ija.2025.15.0014

Productivity of Cultivating Silk Worms (<i>Tubifex</i> sp.) in a Tray System Using Different Doses in The Maintenance Media at Bpbat Mandiangin

2025· article· en· W4412927409 on OpenAlexvenueno aff
Rustiana Widaryati, Darmono Darmono

Bibliographic record

VenueInternational Journal of Aquaculture · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilkworms and Sericulture Research
Canadian institutionsnot available
Fundersnot available
KeywordsSILKTrayBiologyProductivityBotanyChemistryEngineering

Abstract

fetched live from OpenAlex

The success of this aquaculture effort is determined through a handful of activity segments which are the key to the success of an effort to cultivate fish. One natural food that has high nutritional content is silk worms. Silk worms ( Tubifex sp.) are a natural food that is widely used as fish food. The aim is to determine productivity, biomass and increase in worm population. The methods used in this activity are primary data collection, secondary data collection and documentation. The treatments given were treatments A and B, namely in the treatment of probiotics and 10% molasses with a media height of 5 cm and in treatment B probiotics and 20% molasses with a media height of 4 cm. The stages of implementing the activities included preparing containers, preparing culture media, spreading silk worms ( Tubifex sp.), maintenance and harvesting of silk worms. The test parameters observed included absolute biomass, population increase and productivity. The results of the research showed that silk worm productivity, absolute biomass and silk worm population increase with the tray system with different doses in the highest media was in treatment A with a productivity value of 3.8 kg/m 2 , total biomass 164 grams and population increase of 138 720 ind/m 2 .

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 score0.905
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.265
Teacher spread0.246 · 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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