Visualizing The Big Three: Geospatial Interpolation Of Heavy Metal Sediment Contamination In Lake Erie
Bibliographic record
Abstract
A wealth of resources for economic prosperity have driven development along the shorelines of the Great Lakes for over 150 years. The rapid growth of industrial, agricultural, and residential land use has degraded many natural components of lake ecosystems, including sediments and water quality. In this dissertation, spatiotemporal patterns of non-essential heavy metal sediment contamination in Lake Erie will be examined from historic and contemporary sediment surveys. Three inter-related studies explore innovative methods for improving the validity and overall usefulness of sediment contamination maps that could be used by a variety of stakeholders in pollution control efforts throughout the Lake Erie basin. First, sediment survey designs are analyzed for their utility in creating valid interpolated surfaces from which spatiotemporal comparisons of mercury sediment contamination can be compared over time. The next study explores how ancillary sediment variables and contamination categorization methods can support interpolated maps of cadmium sediment contamination from low-density sediment surveys. The final study introduces a novel method of three-dimensional geovisualization to enhance the geographic representation of lead sediment contamination patterns throughout the Lake Erie basin. Innovative research methodologies designed for this dissertation may be applied to sediment contamination studies in other Great Lakes. The visualization techniques employed in mapping sediment contamination patterns provide strong scientific evidence for spatiotemporal change in non-essential heavy metal pollution throughout Lake Erie. Combined, the research findings and maps produced throughout this dissertation can contribute to the growing body of knowledge used in environmental decision making for pollution control in the Great Lakes basin
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".