Bacterial, Archaeal, and Eukaryote Diversity in Planktonic and Sessile Communities Inside an Abandoned and Flooded Iron Mine (Quebec, Canada)
Bibliographic record
Abstract
Abandoned and flooded ore mines are examples of hostile environments (cold, dark, oligotrophic, trace metal) with a potential vast diversity of microbial communities rarely characterized. This study aimed to understand the effects of depth, the source of water (surface or groundwater), and abiotic factors on the communities present in the old Forsyth iron mine in Quebec (Canada). Water and biofilm samples from the mine were sampled by a team of technical divers who followed a depth gradient (0 to 183 m deep) to study the planktonic and sessile communities’ diversity and structure. We used 16S/18S rRNA amplicon to characterize the taxonomic diversity of Bacteria, Archaea, and Eukaryotes. Our results show that depth was not a significant factor explaining the difference in community composition observed, but lifestyle (planktonic/sessile) was. We discovered a vast diversity of microbial taxa, with taxa involved in carbon- and sulfur-cycling. Sessile communities seem to be centered on C1-cycling with fungi and heterotrophs likely adapted to heavy-metal stress. Planktonic communities were dominated by ultra-small archaeal and bacterial taxa, highlighting harsh conditions in the mine waters. Microbial source tracking indicated sources of communities from surface to deeper layers and vice versa, suggesting the dispersion of organisms in the mine, although water connectivity remains unknown.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".