Biofilms for Turbidity Mitigation in Oil Sands End Pit Lakes
Bibliographic record
Abstract
End pit lakes (EPLs) have been proposed as a method of reclaiming oil sands fluid fine tailings (FFT), which consist primarily of process-affected water and clay- and silt-sized particles. Base Mine Lake (BML) is the first full-scale demonstration EPL and contains thick deposits of FFT capped with water. Because of the fine-grained nature of FFT, turbidity generation and mitigation in BML are issues that may be detrimental to the development of an aquatic ecosystem in the water cap. Laboratory mixing experiments were conducted to investigate the effect of mudline biofilms made up of microbial communities indigenous to FFT on mitigating turbidity in EPLs. Four mixing speeds were tested (80, 120, 160, and 200 rpm), all of which are above the threshold velocity required to initiate erosion of FFT in BML. These mixing speeds were selected to evaluate (i) the effectiveness of biofilms in mitigating turbidity and (ii) the mixing speed required to ‘break’ the biofilms. The impact of biofilm age (10 weeks versus 20 weeks old) on turbidity mitigation was also evaluated. Diverse microbial communities in the biofilms included photoautotrophs, namely cyanobacteria and Chlorophyta (green algae), as well as a number of heterotrophs such as Gammaproteobacteria, Desulfobulbia, and Anaerolineae. Biofilms reduced surface water turbidity by up to 99%, depending on the biofilm age and mixing speed. Lifting and layering in the older biofilms resulted in weaker attachment to the FFT; as such, younger biofilms performed better than older biofilms. However, older biofilms still reduced turbidity by 69% to 95%, depending on the mixing speed. These results indicate that biostabilization is a promising mechanism for turbidity mitigation in EPLs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".