High manganese and nickel concentrations in human hair and well water and low calcium concentration in blood serum in a pristine area with sulphide-rich bedrock
Bibliographic record
Abstract
We report the trace element status of residents living in areas with naturally sulphide-rich bedrock and soil in two municipalities in Finland, Sotkamo and Kaavi. Altogether, 225 people from these sparsely populated regions participated voluntarily by providing hair and blood samples. The concentrations of calcium, zinc and copper in serum as well as selenium and cadmium in whole blood did not show correlation with those concentrations in hair samples. Calcium concentration in serum was slightly lower in the sulphide-rich areas (median value 91.4 mg/l, n = 103) than in the areas with adjacent sulphur-poor bedrock (median value 93.6 mg/l, n = 82). The concentrations of Ni and Mn in hair correlated with those in drinking water. The highest Mn and Ni concentrations in the water samples from private wells were 1620 µg/l and 51 µg/l and the highest concentrations in human hair samples 36.44 mg/kg and 12.3 mg/kg, respectively. The challenge with elevated trace element concentrations in some well waters is well documented. In northern countries (Finland, Sweden, Norway and Canada), only 10% of the population depend on private well water, and 90% have access to monitored municipal water supplies. Compared with data available from sulphide mine sites globally, the nickel and manganese concentrations in human hair samples were high in our sulphide-rich study area at Sotkamo representing the trace element status of residents under natural conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".