Assessing historical versus contemporary mercury and lead contamination in Lake Huron sediments
Bibliographic record
Abstract
This research utilized surficial sediment core sample data that were collected in 1969/1973 and 2002 from Lake Huron as part of the Environment Canada Great Lakes Sediment Assessment Program. Concentrations for mercury and lead were analyzed due their persistence in the lake ecosystem and their detrimental environmental effects. The analysis area included the main basin of Lake Huron, Georgian Bay, and the North Channel. Comprehending overall pollution levels strictly on the basis of point data is a difficult task, however spatial analysis techniques combined with Geographic Information Systems can be used to gain a better understanding of lake-wide trends. The Geostatistical Analyst extension of the ESRI ArcGIS software was used to carry out ordinary kriging analyses on the datasets. They produced statistically valid concentration estimates with log-normal data transformation procedures occasionally being performed to obtain suitable prediction estimates. Geospatial analysis (including kriging) allows for samples that vary in number and location to be analyzed and compared with each other based on areal estimates. Overall decreases in contamination levels were observed between the historical and contemporary surveys. Mercury has seen a dramatic reduction in concentrations from 1969/1973 to 2002, while the lead results indicate that high levels of contamination (compared to background concentrations) still persist in the contemporary dataset, although they have subsided from historic values. Higher contaminant concentrations were generally found in depositional basins. The interpolated kriging surfaces are more informative than i.e. conventional dot and/or proportional circle maps in the amount of information they present. They also provide an increased understanding of both the spatial distribution and temporal trends in sediment contamination in Lake Huron.
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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 | 0.000 | 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".