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Small 110-year old mine in northern Sweden leaves disproportionately high metal impact on water quality

2020· article· en· W3096139570 on OpenAlexaboutno aff
Sandra Fischer, Gunhild Rosqvist, Sergey Chalov, Magnus Mörth, Jerker Jarsjö

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCopper mineEnvironmental scienceWater qualityPollutionHydrology (agriculture)Mining engineeringGeographyGeologyCopperChemistryEcology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.274
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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