Source-Specific Patterns of Marine Debris and Associated Ecological Impacts in the Red River Estuary of Xuan Thuy National Park, Vietnam
Bibliographic record
Abstract
Vietnamese rivers are among the top ten contributors of anthropogenic debris to the ocean. However, there is limited empirical research documenting debris and its effects in Northern Vietnam. The goal of our research was to conduct the first baseline assessment of anthropogenic debris in the Red River. We aimed to understand the sources, accumulation patterns, and ecological effects of anthropogenic debris in the Red River (Song Hong) estuary. To assess debris patterns, we conducted standing stock debris surveys at sites in the mouth, and upstream of the Red River. To assess the ecological effects of anthropogenic debris on mangrove ecosystems, we measured mangrove diameter, canopy cover, and number of crab burrows/m2 in the same debris transects. Anthropogenic debris was found at all sites, and plastic was the most common material. We identified a non-significant trend, whereby ecological indices declined with increasing amounts of debris. Overall, our results demonstrate that anthropogenic debris is ubiquitous in the Red River estuary, composition varies among sites, and this debris may have adverse or neutral ecological effects on mangrove ecosystem health. Future work should conduct debris assessments at larger spatial scales, and assess ecological responses at the community or population level over extended time periods.
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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 | 0.000 | 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".