Rural plastic emissions into the largest mountain lake of the Eastern Carpathians
Bibliographic record
Abstract
The lack of proper waste collection systems leads to plastic pollution in rivers in proximity to rural communities. This environmental threat is more widespread among mountain communities which are prone to frequent flash floods during the warm season. This paper estimates the amounts of plastic bottles dumped into the Izvoru Muntelui lake by upstream rural communities. The plastic pollution dimension between seasonal floods which affected the Bistrita catchment area during 2005-2012 is examined. The floods dumped over 290 tonnes of plastic bottles into the lake. Various scenarios are tested in order to explain each amount of plastic waste collected by local authorities during sanitation activities. The results show that rural municipalities are responsible for 85.51% of total plastic bottles collected during 2005-2010. The source of plastic pollution is mainly local. The major floods of July 2008 and June 2010 collected most of the plastic bottles scattered across the Bistrita river catchment (56 villages) and dumped them into the lake. These comparisons validate the proposed method as a reliable tool in the assessment process of river plastic pollution, which may also be applied in other geographical areas. Tourism and leisure activities are also found to be responsible for plastic pollution in the study area. A new regional integrated waste management system should improve the waste collection services across rural municipalities at the county level when it is fully operational. This paper demonstrates that rural communities are significant contributors of plastics into water bodies.
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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) | 0.002 | 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".