Riverine Macro-litter: Plastic Pollution in Different Tributaries of the Ishëm River (Albania)
Bibliographic record
Abstract
According the Ocean Cleanup Ishëm River is the most polluted river in Europe with 733,000 kg of solid waste per year ending up in the Adriatic Sea.The Ishëm mouth is inside the MPAs Patok-Fushëkuqe-Ishëm with a surface of 5,001 ha under protection status: Managed Nature Reserve IUCN Category IV and Important Bird Area (AL006).Additionally Cape Rodon Nature Reserve is located within the Protected Landscape/Seascape Area of the Cape of Rodon-Lalzi Bay-Ishmi Forest with a surface area of 2,500 ha and classified under the IUCN Category V.In 2020, River-Cleanup.org has come to Albania to change the history of Ishëm River on plastic pollution together with Albanian people, especially the youth awareness, to protect the beauties of their natural resources.The riverine litter surveys were carried out on riverbanks at four study sites along Ishëm River, at Lana Stream, Tirana River, Limuth Stream and Gjola River.Study sites were randomly selected along the riverbank, parallel to the waterline, with a stretch of 100 m long.The mean macro-litter density of the Ishëm River became 0.992 items/m 2 and 1,269 items/100 m.The largest part of riverine litter items at the aggregated level were made of artificial polymer materials (82%).What we found mostly in the Ishem River about items there are G7 (drink bottles <=0.5l),G3 (shopping bags, incl.pieces), G30 (crisps packets/sweets wrappers) and G8 (drink bottles >0.5l).
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".