Nearshore Sedimentary Mercury Concentrations Reflect Legacy Point Sources and Variable Sedimentation Patterns Under a Natural Recovery Strategy
Bibliographic record
Abstract
Abstract The St. Lawrence River at Cornwall, Ontario, Canada, received substantial inputs of mercury from local, shoreline-based industries through much of the 20th century. Although emission controls were implemented in the late 20th century to reduce the influx of mercury and other metals entering the river, legacy contamination of riverine sediments continues to be a concern. Monitored natural recovery was prescribed in 2005 to remediate contaminated sediments; however, few surveys have been undertaken to examine its effectiveness on shallow, nearshore sediments in contaminated areas. Surface sediments were collected at shallow, nearshore sites in contaminated zones and upstream reference areas to evaluate the current state of sedimentary contamination of mercury and other metals. A Getis–Ord Gi* “hot spot” analysis was employed to assess the spatial distribution of contaminants. In addition, 3 sediment cores were collected from contaminated zones and dated using radioisotopes (210Pb) to assess sedimentation patterns over time. Results indicated that surface sediments from contaminated zones remained elevated in mercury relative to reference sites but spatial distribution of contaminants was highly heterogeneous. Dated sediment cores suggested that sedimentation was not occurring consistently across all areas; variable sedimentation and resuspension patterns over small spatial scales were likely factors driving heterogeneous sedimentary contamination. Such patterns complicate remediation strategies because unburied sediments may serve as continuing sources of contaminants to the ecosystem. Environ Toxicol Chem 2021;40:1788–1799. © 2021 SETAC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".