High Diversity in Iron Cycling Microbial Communities in Acidic, Iron-Rich Water of the Pyhäsalmi Mine, Finland
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Bibliographic record
Abstract
Microbial communities of iron-rich water in the Pyhäsalmi mine, Finland, were investigated with high-throughput amplicon sequencing and qPCR targeting bacteria, archaea, and fungi. In addition, the abundance of Leptospirillum and Acidithiobacillus was assessed with genus-specific qPCR assays, and enrichment cultures targeting aerobic ferrous iron oxidizers and ferric iron reducers were established. The acidic (pH 1.4–2.3) mine water collected from 240 m, 500 m, and 600 m depth from within the mine had a high microbial diversity consisting of 63-114 bacterial, 10-13 archaeal, and 104-117 fungal genera. The most abundant microorganisms in the mine water were typical acid mine drainage (AMD) taxa, such as acidophilic, iron-oxidizing Leptospirillum , Acidiphilum , Acidithiobacillus , Ferrovum , and Thermoplasma . The fungi belonged mostly to the phylum Ascomycetes, although a great part of the fungal sequences remained unclassified. The number of archaeal 16S rRNA genes in the mine water was between 0.3 and 1.2 × 10 7 copies mL −1 in the samples from 500 m and 600 m, but only 3.9 × 10 3 at 240 m and archaea were in general not enriched in cultures. The number of fungal 5.8S rRNA genes was high only in the mine water from 500 m and 600 m, where 0.2–3.4 × 10 4 spore equivalents mL −1 were detected. A high number of Leptospirillum 16S rRNA genes, 0.6–1.6 × 10 10 copies mL −1 , were detected at 500 m and 600 m depth and in cultures containing ferrous iron, showing the importance of iron oxidizers in this environment. The abundance of bacteria in general was between 10 3 and 10 6 16S rRNA gene copies mL −1 . Our results showed a high microbial diversity in the acid- and iron-impacted waters of the Pyhäsalmi mine, where Leptospirillum bacteria were especially prominent. These iron oxidizers are also the main nitrogen-fixing microorganisms in this ecosystem.
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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 it