Assessing Lead Contamination in Buffalo River Sediments
Bibliographic record
Abstract
<p>The Great Lakes Water Quality Agreement between Canada and the United States has identified the Buffalo River as an Area of Concern. The watershed has a long history of heavy industrial activity that contributed to its overall pollution. Sediment core data collected by the New York State Department of Environmental Conservation in 2005 were used to determine lead sediment conta- mination in a section of the Buffalo River. The ordinary kriging spatial interpolation technique was used to generate surface and sub- surface sediment contamination estimates. Due to the meandering nature of the river, two kriging models were used to analyze surface contamination: a global kriging model and a regional kriging model, consisting of three separate sections. The results show that both the global and regional kriging models display similar interpolated surfaces and do not vary significantly. Within the sediment, lead contamination in the surface layer is lower than at the various subsurface depths. In 2011, habitat restoration efforts commenced to re- mediate environmental damage due to years of pollution inputs from various sources. Sediment dredging operations were initiated that are expected to be completed in 2015. The goal of these operations is to remove heavily contaminated sediments and rehabilitate the Buffalo River. The kriging results provide area-wide estimates of contamination. When compared to the dredging plan, the results indicate that additional removal of contaminated sediments may need to be considered where no dredging has occurred or is not cu- rrently planned. </p>
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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 | 0.000 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".