Comparison of Microbial Biomass, Biodiversity, and Biogeochemistry in Three Contrasting Gold Mine Tailings Deposits
Bibliographic record
Abstract
Abstract An interdisciplinary approach was used to assess the biogeochemistry of three deposits of gold mine tailings in Nopiming Provincial Park, Manitoba, Canada. Each depositional site has developed varying levels of natural revegetation over the past 70 years. Although the tailings are the products of processing similar carbonate-hosted quartz-carbonate shear zones by the same methods, the physical, chemical, and hydrogeological conditions varied among sites. The sample from the barren tailings area at the Central Manitoba site was lower in pH (4.87 ± 1.34) and higher in total sulfur (337 ± 166 μmol/g) and copper (44.5 ± 20.9 μ mol/g) than samples from the other two sites. Microbial activities have impacted the biogeochemical distribution of carbon, sulfur (total, sulfide, sulfate), and iron (total, Fe(II)) in the tailings at all three sites. The microbial communities were distributed throughout the tailings, but the biomass and biodiversity were greatest at the surface in the revegetated (Ogama-Rockland) and partially revegetated (Gunner) tailings. In contrast, the most barren set of tailings (Central Manitoba) had the greatest biomass and biodiversity in the middle layer (15 cm depth), which also had the greatest abundance of metals, anions, and carbon. The distribution of fatty acid methyl esters (FAME) in the tailings was dependent on both the depth and the individual characteristics of the site. The biomass and biodiversity correlated with the physicochemical conditions, particularly as affected by water movement and hydrology. The primary determinants limiting natural attenuation of the sites may be insufficient calcite buffering, hydrogeology, and the distribution of microbes, rather than a lack of microbes.
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 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".