Evaluating the Metal Tolerance Capacity of Microbial Communities Isolated from Alberta Oil Sands Process Water
Bibliographic record
Abstract
Anthropogenic activities have resulted in the intensified use of water resources. For example, open pit bitumen extraction by Canada's oil sands operations uses an estimated volume of three barrels of water for every barrel of oil produced. The waste tailings-oil sands process water (OSPW)-are stored in holding ponds, and present an environmental concern as they are comprised of residual hydrocarbons and metals. Following the hypothesis that endogenous OSPW microbial communities have an enhanced tolerance to heavy metals, we tested the capacity of planktonic and biofilm populations from OSPW to withstand metal ion challenges, using Cupriavidus metallidurans, a known metal-resistant organism, for comparison. The toxicity of the metals toward biofilm and planktonic bacterial populations was determined by measuring the minimum biofilm inhibitory concentrations (MBICs) and planktonic minimum inhibitory concentrations (MICs) using the MBEC ™ assay. We observed that the OSPW community and C. metallidurans had similar tolerances to 22 different metals. While thiophillic elements (Te, Ag, Cd, Ni) were found to be most toxic, the OSPW consortia demonstrated higher tolerance to metals reported in tailings ponds (Al, Fe, Mo, Pb). Metal toxicity correlated with a number of physicochemical characteristics of the metals. Parameters reflecting metal-ligand affinities showed fewer and weaker correlations for the community compared to C. metallidurans, suggesting that the OSPW consortia may have developed tolerance mechanisms toward metals present in their environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".