Macroplastics and Microplastics in Intertidal Sediment of Vinces and Los Tintos Rivers, Guayas Province, Ecuador
Bibliographic record
Abstract
The composition, abundance and distribution of macroplastics (MAPs) and microplastics (MPs) in the Vinces and Los Tintos rivers were determined in three sites (Pueblo Nuevo, Santa Marianita, Los Tintos) from the low basin in the coastal province of Guayas, Ecuador. MAPS were recorded by visual census, covering a total distance of 140 m, and MPs were extracted in the intertidal sediments via density separation using a saturated NaCl solution, and these were counted using a stereomicroscope. A total of 940 plastic items were identified. The predominant debris was plastic with 85.2%, followed by manufactured materials and metals. The Vinces River contained the highest abundance of plastic in the locality of Pueblo Nuevo. The most abundant plastic was MPs. The most common MAPs were plastic bags (23%), food packaging (17%) and foamed plastic (8%). MP size classes quantified between 0.15 and 2.52 mm in intertidal, very fine sandy sediment and decreased in abundance with increasing grain size. The most common MPs were fibres (65.2%) (black (43.8%) and blue (25.8%)), and their distribution has a high correlation with population density and water flow direction: Santa Marianita 5.55 g−1, Pueblo Nuevo 7.39 g−1, Los Tintos 8.17−1. A significant abundance of fibres was identified in Pueblo Nuevo. The plastic spatial distribution revealed major plastic pollution in areas where recreational and tourism activities have been developed. Therefore, we recommend implementing awareness campaigns by educating businesses, residents and tourists on managing solid waste (especially plastic) and wastewater. Our results can serve as a baseline for future plastic monitoring in the area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".