Small 110-year old mine in northern Sweden leaves disproportionately high metal impact on water quality
Bibliographic record
Abstract
Pollution from small abandoned mines is usually overseen compared to larger historical mining sites. Especially in the Arctic more research is needed on long-term water quality degradation from mine waste (e.g. metal leakage). We have studied changes in water quality from a historical copper (Cu) mining area, Nautanen, northern Sweden, that was shortly in operation for six years before abandonment in 1908 (~110 years ago). Water quality data from previous studies of the site (1993-2014) was compared to results from our own field campaigns in 2017, which provided us with a rare Arctic case study of 25 years of data. The results showed Cu, Zn and Cd concentrations at the mining zone being orders of magnitude larger than local background levels. This was surprising considering Nautanen’s short time of operation, the small scale of the mining site, and the long time since closure. We found no declining trend of metal concentrations over the surveyed 25-year period (1993-2017) and during the past 110 years (1907-2017) a mass flow of 43 tons of Cu was estimated to have been released to the local surface water system from the mining zone and 7 tons of Cu at 4 km downstream. Nautanen stands out with its high metal leakage relative to its small volume of mine waste compared to mass flows of other larger historical mining sites in e.g. Sweden and Canada. Small abandoned sites, which are numerous, could add disproportionately large amounts of metals to surface water systems. This information is crucial in upscaling local low-priority sites to regional assessments of total pollution pressures in sensitive Arctic environments. We are currently further investigating pollution transport pathways through oxygen and sulfur isotopes to trace water originating from the mine and other sources (e.g. atmospheric deposition, bacterial sulfate reduction). This method could give valuable information in data scares sites where e.g. groundwater data is inaccessible.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".