Special issue on mercury in Canada's North: Summary and recommendations for future research
Bibliographic record
Abstract
Important scientific advances have been made over the last decade in identifying the environmental fate of mercury and the processes that control its cycling in the Canadian Arctic. This special issue includes a series of six detailed reviews that summarize the main findings of a scientific assessment undertaken by the Government of Canada's Northern Contaminants Program. It was the first assessment to focus exclusively on mercury pollution in the Canadian Arctic. Key findings, as detailed in the reviews, relate to sources and long-range transport of mercury to the Canadian Arctic, its cycling within marine, freshwater, and terrestrial environments, and its bioaccumulation in, and effects on, the biota that live there. While these accomplishments are significant, the complex nature of the mercury cycle continues to provide challenges in characterizing and quantifying the relationships of mercury sources and transport processes with mercury levels in biota and biological effects of mercury exposure. Of particular concern are large uncertainties in our understanding of the processes that are contributing to increasing mercury concentrations in some Arctic fish and wildlife. Specific recommendations are provided for future research and monitoring of the environmental impacts of anthropogenic mercury emissions, influences of climate change, and the effectiveness of mitigation strategies for mercury in the Canadian Arctic.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".