Sediment Interfacial Interactions Controlling Nutrient and REDOX Flux within Experimental End Pit Lake Tailings
Bibliographic record
Abstract
The recovery of bitumen from Alberta Oil sands generates enormous volumes of oil sands process material (OSPM). After bitumen extraction, tailings are pumped into retention ponds, where the sand fraction settles, and most of the aqueous slurry (i.e. fines consisting of silts, clays and residual hydrocarbons) slowly densifies which is termed mature fine tailings (MFT). Long term Reclamation management strategies focus on the deposition of this material within large end pit lakes using a CT process. However questions still remain regarding the function of chemical and biological constituents within the MFT during maturation of developing end pit lake ecosystems. A number of processes can occur within the MFT that will affect both volume and water cap quality. This may include possible phsyico –chemical alteration of dissolved constituents within the MFT driven by reduction- oxidation reactions possibly controlling consolidation, water cap quality and microbial community structure. In this study laboratory microcosms containing fresh MFT were used to investigate the chemical and biological controls affecting the REDOX chemistry of the MFT and establish the role of developing microbial communities within new MFT sediment upon aging. Changes in the principal chemical, physical and biological populations of the MFT will be assessed under aerobic and anaerobic conditions using a combination of microelectrode arrays and DNA profiling at the tailings water interface. The results presented will discuss some of the preliminary findings along with novel technique development to assess bench scale tailings characterization and their impact on sediment oxygen demand (SOD) for future end pit lake model behaviour. We also discuss how our laboratory based microcosm results can be validated under field conditions.
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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 | 0.000 |
| 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".