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Record W4408513731 · doi:10.21107/rekayasa.v16i3.19543

Kontaminasi Mikroplastik pada Ikan Kiper (Scatophagus argus) dari Laguna Segara Anakan, Cilacap

2023· article· id· W4408513731 on OpenAlexaff
Nuning Vita Hidayati, Siti Hotijah, Mohammad Nuh Hudawi, Sapto Andriyono, Dyahruri Sanjayasari, Dewi Wisudyanti Budi Hastuti, Hendrayana Hendrayana

Bibliographic record

VenueRekayasa · 2023
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsArgusZoologyComputer scienceBiologyProgramming language

Abstract

fetched live from OpenAlex

Microplastics are pollutants of emerging concern today. The presence of microplastics in fish from several marine environments has been reported worldwide. This study examined the presence of microplastics in the gastrointestinal tract (GIT) of the Kiper Fish (Scatophagus argus) from the Segara Anakan Lagoon, Cilacap, Central Java. Microplastics were found with an average abundance of 22.22 ± 6.8 items/ind. Fragment (45%) was the main type of microplastic found in the analyzed Kiper fish, followed by fiber (27%), film (21%), and pellets (7%). Eight types of colors were found in the analyzed fish, with black (43%) and transparent (33%) being the predominant plastic colors. There were 12 types of microplastic polymers found, namely Polystyrene (PS), Nylon, Polymethyl methacrylate (PMMA), Cellulose acetate (CA), Polycarbonate (PC), Polyvinyl chloride (PVC), Polypropylene (PP), Polyurethane (PU), Latex, Acrylonitrile butadiene stryrene (ABS), High-density polyethylene (HDPE), Polyethylene telephthalate (PETE). The results of this study indicate that more serious attention must be paid to the handling of plastic waste, given the accumulation of high amounts of microplastics in fish, which can be harmful to human health.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.007

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 head0.031
GPT teacher head0.235
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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