Data for: Evaluating spatiotemporal patterns of arsenic, antimony, and lead deposition from legacy gold mine emissions using lake sediment records
Bibliographic record
Abstract
In the absence of long-term environmental monitoring prior to and during resource development, identifying the extent of pollution is challenging but important for assessing risks to ecosystem health. Legacy pollution from Giant Mine in the Northwest Territories is a concern because while gold smelting operations ceased in the late 1990s, the fine, toxic dust arsenic trioxide dispersed into the atmosphere, potentially creating repositories in the surrounding landscape. Lake water surveys and the sampling of surficial sediment have identified a confined emissions footprint within a 30-km radius of the mine. However, these measurements may not capture the range of aerial deposition of emissions from the mine, particularly peak emissions released during the 1950s. Paleolimnological studies from far-field locations have shown evidence of arsenic enrichment coinciding with the timing of peak mine emissions during the 1950s, suggesting further research is needed to characterize stores of legacy metals derived from Giant Mine pollution.To address this need, as part of the Sub-Arctic Metal Mobility Study, temporal patterns of metals (arsenic, antimony, and lead) deposition and hydrological conditions were reconstructed from sediment cores collected from eight lakes along an 80-km transect northwest of Yellowknife, following the prevailing wind direction. Two sediment cores were collected from each lake by using a Uwitec gravity corer fitted with PVC tubes (86-mm internal diameter). These lake sediment cores provide further characterization of the Giant Mine emission footprint, and the depositional and post-depositional history of arsenic and other metals in sub-arctic lakes and their catchments.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".