Metals in the sediments of the Huron‐Erie Corridor in North America: Factors regulating metal distribution and mobilization
Bibliographic record
Abstract
Abstract Sediment samples from the Huron‐Erie Corridor (Great Lakes, North America) were collected to quantify the relative importance of natural and anthropogenic sources of contamination, and to study the spatial metal distribution patterns of metals as a function of the characteristics of the Corridor sediments. A stratified random sampling design was used to measure the spatial patterns of metal inputs, settling and sorting along the length of the Corridor. Factors regulating metal mobilization were assessed by determining metal affinities with the total organic fraction (TOM), the mineral fraction (represented as Al), and the granulometric characteristic (represented as <0.063 mm fraction). The study revealed that anthropogenic factors primarily regulated metal distributions and mobilization throughout the Huron‐Erie Corridor. In the St. Clair and Detroit Rivers, the spatial pattern of metal distributions strongly reflected local industrial sources. In the Walpole Delta and Lake St. Clair, however, inorganic (clays) and organic (TOM) particles dominated the contaminant distribution. Sediment contamination issues throughout the Huron‐Erie Corridor were dominated by mercury, released from sources along the St. Clair and Detroit Rivers. The mean enrichment factor EFAl for mercury in these sediments has reached 68.3. Other metal pollutants were confined to the sediments in the lower depositional reach of the Corridor.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".