Spatial distributions and temporal trends in pollutants in the Great Lakes 1968–2008
Bibliographic record
Abstract
The Great Lakes have been the focus of intensive long-term research and monitoring programmes for the past 40 years. Spatial distributions and temporal trends have been determined for a range of environmental compartments, including surface water, sediment and fish. In general, there have been dramatic reductions in contamination by legacy pollutants including polychlorinated biphenyls (PCBs), organochlorine pesticides and metals. Concentrations of PCBs and lead in surface water at the mouth of the Niagara River have decreased by 58 and 54%, respectively, over the period 1986–2007. Correspondingly, concentrations of PCBs and lead in offshore sediments of Lake Ontario have decreased by 37 and 45%, respectively, since peak accumulations in the 1970s. Temporal trends for more modern chemicals, including polybrominated diphenylethers and perfluoroalkyl compounds, showed increases up until 2000 when management actions and heightened stakeholder awareness resulted in a levelling off or decline in the subsequent time period. While legacy issues are largely associated with areas of historical industrial activity, the presence of newer chemicals is generally associated with modern urban/industrial areas that act as diffuse sources.
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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 | 0.000 | 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".