Microbial community development in an oil sands pit lake
Bibliographic record
Abstract
Surface mining and extraction of oil sands in Canada produces fluid tailings that contain several compounds of concern for the environment. One option for mine reclamation is the construction of Pit Lakes (PLs) to contain and remediate these tailings. Ultimately, PLs should support food webs typical of boreal lakes. From 2015 to 2021, we applied 16S/18S rRNA gene amplicon sequencing and metagenomics to monitor prokaryotic and eukaryotic microbes in the only full-scale PL of the oil sands industry (Base Mine Lake or BML), and compared it to two control environments: a freshwater reservoir unaffected by tailings, and active tailings ponds receiving regular industrial input. Microbial communities in BML were always intermediate to the two control environments based on alpha and beta diversity analyses. BML communities were highly variable with year, season, and water depth, and contained fewer core species than the freshwater reservoir. Several hydrocarbon degraders and sulfur cycling bacteria were identified as indicator species of tailings ponds, while several phototrophs were indicative of freshwater. However, all of these species were abundant in BML, suggesting that the PL supports food webs characteristic of each control environment. Over the 6-year study, the relative abundances of some common freshwater phytoplankton (Cryptomonas, Mychonastes, Trebouxiophyceae, Cyanobium) and heterotrophic bacteria (Sporichthyaceae, Ca. Fonsibacter, Ilumatobacteraceae, Microbacteriaceae, Ca. Planktophila) increased in BML. The results suggest that microbial communities and processes in BML represent an intermediate state between a tailings pond and a natural freshwater system, and did not stabilize within 10 years of its creation.
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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 | 0.001 |
| 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".