Preliminary Study of Microplastic Abundance in Rivers of Greater Semarang Area, Indonesia
Bibliographic record
Abstract
Microplastics are concerning pollutants with increasing global presence.Yet, data on their occurrence in Indonesian rivers, especially in Semarang, is limited.This study aimed to assess microplastic abundance and characteristics in Greater Semarang's Babon and East Flood Canal (Kanal Banjir Timur, KBT) rivers at five stations.Sampling and analysis followed Japanese Guidelines of Riverine Microplastic Survey.Spearman's correlation analyzed the link between microplastic abundance and population served by waste services.Microplastics were found in all stations, with the highest levels downstream, likely due to accumulation along the flow.Abundance varied from 1.1-9.6 particles/m 3 .No significant correlation between microplastic abundance and population was found.The most prevalent microplastic form was sheet (14-75%), primarily sized 0.5-1 mm and black (1-60%).The main identified polymer type was polyethylene in sheet form, possibly originating from single-use plastic bags.These findings underscore the urgency of preventing waste leakage into rivers to reduce microplastic release into the environment.By addressing the issue of single-use plastics in hard-to-reach areas and improving waste management practices, we can work towards mitigating the impact of microplastics on the environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".