Enriching acid rock drainage related microbial communities from surface-deposited oil sands tailings
Bibliographic record
Abstract
Little is known about the microbial communities native to surface-deposited pyritic oil sands tailings, an environment where acid rock drainage (ARD) could occur. The goal of this study was to enrich sulfur-oxidizing organisms from these tailings and determine whether different populations exist at pH levels 7, 4.5, and 2.5. Using growth-based methods provides model organisms for use in the future to predict potential activities and limitations of these organisms and to develop possible control methods. Thiosulfate-fed enrichment cultures were monitored for approximately 1 year. The results showed that the enrichments at pH 4.5 and 7 were established quicker than at pH 2.5. Different microbial community structures were found among the 3 pH environments. The sulfur-oxidizing microorganisms identified were most closely related to Halothiobacillus neapolitanus, Achromobacter spp., and Curtobacterium spp. While microorganisms related to Chitinophagaceae and Acidocella spp. were identified as the only possible iron-oxidizing and -reducing microbes. These results contribute to the general knowledge of the relatively understudied microbial communities that exist in pyritic oil sands tailings and indicate these communities may have a potential role in ARD generation, which may have implications for future tailings management.
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 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 | 0.000 |
| 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".