Comparison of Mercury and Lead Sediment Concentrations in Lake Ontario (1968-1998) and Lake Erie (1971–1997/98) using a GIS-Based Kriging Approach
Bibliographic record
Abstract
Abstract This research analyzed sediment contamination concentrations for mercury and lead in Lakes Ontario and Erie using a GIS-based kriging approach. Environment Canada provided sediment survey data for Lake Ontario (1968 and 1998) and Lake Erie (1971 and 1997/98). Collation and mapping of point measurement data without the application of interpolation methods does not allow for spatial data trends to be fully analyzed. The kriging technique enables the creation of interpolated prediction surfaces, with the advantage that the results can be statistically validated. Although data normality is not required, the kriging results for the historical datasets suggest that it may be desirable, as statistical validity was reduced due to some individual stations having very high contaminant concentrations. Three of the four models developed for the 1997/98 data were statistically valid. For both lakes, the more recent data reveal reduced concentrations of mercury and lead, and there has been an overall reduction in contamination levels. However, sediments in some areas still exceeded Canadian sediment quality guidelines. The areas of greatest sediment contamination in Lake Ontario were within the major depositional basins, presumably as a result of historical industrial activities in watersheds along the southern and western shoreline including the Niagara River. In Lake Erie, areas of greatest sediment contamination continue to be located in the western and south central portions of the lake in proximity to the Detroit River and major urban/industrial centres.
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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 | 0.002 |
| 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) | 0.001 | 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".