A temporal assessment of anthropogenic marine debris on sandy beaches from Ecuador’s southern coast
Bibliographic record
Abstract
Anthropogenic marine debris (AMD) is an environmental pollution that affects marine life, human health, wellbeing, and the economy. This marine litter can deposit in the coastlines, particularly on tidal zones and beaches. To pursue future mitigation strategies to reduce AMD is important to monitor the amount, type and frequency of litter being dumped on shores. This study presents the composition, temporal distribution, abundance and size of AMD on three sandy beaches from Guayas province, Ecuador. The field data was recollected from December 2018 to February 2020. A total of 12,362 items of AMD were collected with an abundance of 1.95 macro-litter items/m2. The composition of AMD was marked by the predominance of plastic items (91.8%), followed by wood and cloth (1.9%), while cigarettes were only present in village beaches. Our results suggest that sites with more AMD abundance are beaches nearby small coastal villages and fishing communities. Also, the AMD abundance is slightly higher at the beginning of the dry season than in the rainy season. Our findings indicate that it is necessary to implement concerted solid waste management measures and proactive environmental education programs to empower the local population, as well as investigate the anthropogenic sources and other variables influencing the AMD abundance coming onto sandy shores.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".