Isotopic and Chemical Assessment of the Dynamics of Methane Sources and Microbial Cycling during Early Development of an Oil Sands Pit Lake
Bibliographic record
Abstract
Water-capped tailings technology (WCTT) is a key component of the reclamation strategies in the Athabasca oil sands region (AOSR) of northeastern Alberta, Canada. The release of microbial methane from tailings emplaced within oil sands pit lakes, and its subsequent microbial oxidation, could inhibit the development of persistent oxygen concentrations within the water column, which are critical to the success of this reclamation approach. Here, we describe the results of a four-year (2015–2018) chemical and isotopic (δ13C) investigation into the dynamics of microbial methane cycling within Base Mine Lake (BML), the first full-scale pit lake commissioned in the AOSR. Overall, the water-column methane concentrations decreased over the course of the study, though this was dynamic both seasonally and annually. Phospholipid fatty acid (PLFA) distributions and δ13C demonstrated that dissolved methane, primarily input via fluid fine tailings (FFT) porewater advection, was oxidized by the water column microbial community at all sampling times. Modeling and under-ice observations indicated that the dissolution of methane from bubbles during ebullition, or when trapped beneath ice, was also an important source of dissolved methane. The addition of alum to BML in the fall of 2016 impacted the microbial cycling in BML, leading to decreased methane oxidation rates, the short-term dominance of a phototrophic community, and longer-term shifts in the microbial community metabolism. Overall, our results highlight a need to understand the dynamic nature of these microbial communities and the impact of perturbations on the associated biogeochemical cycling within oil sands pit lakes.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".