Uncovering Hidden Pollution: Diffuse Contaminant Sources in a Sparsely Industrialized Estuarine System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding sediment contamination in low-industrial activity areas remains a critical but understudied issue, particularly in estuarine ecosystems subject to diffuse pollution sources. This study employed nontarget screening with high-resolution mass spectrometry to analyze sediment samples from Ganggu Estuary, South Korea, to evaluate the composition and distribution of sediment contaminants in a region lacking heavy industry but influenced by agriculture, fisheries, and tributary discharges. The investigation revealed that contamination stems from multiple sources, including streams, agricultural runoff, and fish market discharges. A total of 678 chemicals were identified, including human and veterinary drugs (8.7%), food additives (6.5%), pesticides (2.8%), and PFAS (1.9%), with spatial variability confirmed by total organic carbon (TOC) analysis. Notably, this study is the first to report the detection of dinoseb, a banned herbicide in estuarine sediments, identified with high confidence alongside key pollutants such as 6:2 fluorotelomer sulfonic acid and 2,4-di- tert -butylphenol. We linked contamination hotspots to U-shaped river bends, streamwater and sediment inputs, and agricultural runoffs, and highlight the role of natural processes in pollutant deposition in a region where heavy industry is absent, yet diffuse sources still drive significant contamination.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 it