Analyzing the Spatial Distribution of Sediment Contamination in the Lower Great Lakes
Bibliographic record
Abstract
Abstract Despite significant reductions in contaminant concentrations over the past 30 years, large areas within Lake Erie and Lake Ontario still exceed Canadian sediment quality guidelines. Hexachlorobenzene (HCB), polychlorinated biphenyls (PCBs), lead (Pb) and mercury (Hg) can persist for long periods of time in the environment and cause significant ecosystem damage. Analyses of the spatial distribution of these contaminants were carried out using a GIS-based kriging technique. Initially, statistically valid results were obtained for three of four contaminants in Lake Erie (HCB, Pb, Hg) and two of four (HCB, Hg) in Lake Ontario. Acceptable concentration estimates were subsequently achieved for all contaminants following log-normal transformation kriging analyses. In general, the concentration of contaminants was lower in sediment collected in Lake Erie than in Lake Ontario. In many areas of Lake Erie, the concentrations were under both the probable effect level (PEL) and the threshold effect level (TEL), which relate to the severity of adverse biological effects that may be expected. Greater concentrations of these contaminants were observed in Lake Ontario sediments, which can be partly explained by the bathymetry and current circulation patterns in the lake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".