Analyzing the Rare Earth Elements (REEs) and Trace Metals in Tailings
Bibliographic record
Abstract
In the process of producing bitumen from oil sand, a by-product called tailings is produced. Tailings are a mixture of clay, fine particles, water, solvent and residual bitumen. The industry’s current approach is to leave them in tailings ponds; however, that may cause environmental impacts to the ecosystems around them due in part to the toxic trace metals found in them. Research has shown that there are also valuable rare Earth elements (REEs) present in tailings. REEs found in tailings include Cerium, Neodymium, Lanthanum etc. Iron, Titanium, and Zirconium are not considered REEs but are still valuable enough to be extracted. The objective of this research was to determine the concentration of REEs and trace metals in bitumen froth treatment tailings (FTT). Our research team used acid digestion and inductively coupled plasma mass spectroscopy (ICP-MS) to measure the concentration of REEs and trace metals in several samples of FTT ash. We learned that Cerium was the most prevalent REE in tailings samples (>1000ppm), followed by Neodymium and Lanthanum. Zirconium was the most prevalent trace metal found in this tailings sample (>1000ppm), followed closely by Vanadium. Knowing the exact concentration of harmful trace metals in tailings will allow us to determine the extent of tailings ponds environmental effect and toxicity. Collecting and selling expensive metals found in tailings could be the start of a new precious metals economy in Alberta, which would provide new investment opportunities and jobs. This would also encourage corporations to invest in finding new ways to extract these precious metals, resulting in more purified tailings and less tailings overall going into tailings ponds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".