Challenges to Aquatic Food Source Sustainability: Investigating the Bioaccumulation of Microplastics of Tilapia and Mussels
Bibliographic record
Abstract
Microplastics are small plastic particles measuring less than 5 mm in diameter, have emerged as a pervasive environmental pollutant in aquatic ecosystems worldwide. Food from aquatic sources has been contaminated. This study investigates the microplastics from food aquatic resources and the potential consequences for human health. By conducting field sampling and laboratory analysis, the researchers evaluate the distribution and prevalence of microplastics in Tilapia and Mussels caught from Laguna de Bay. The researchers utilized the Fourier-transform infrared spectroscopy on analysing the data and to create a comprehensive interpretation of results. The results show that microplastics are present in the intestine of the caught samples for both Tilapia and Mussels. These findings provide insight into the composition of the sample, highlighting the presence of Polyethylene Terephthalate (PET), with the Infrared spectrum of the sample matches that of chlorinated polyethylene, which is a type of microplastic. This shows that when cooked and eaten, contaminated aquatic food can deliver microplastics to the human body through bioaccumulation. This could also result to biomagnification of the ingested microplastics as the humans consume more of this aquatic food source. When an individual ingests a microplastics it could manifest to diseases and lead to serious complications like Cardiovascular Disease, Impaired Kidney and Liver function, Development of Metabolic Disorders, Neurological Effects and Reproductive and Developmental Issues. Further evaluation on the types of microplastics present among other aquatic food sources must be investigated as well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".