Assessing and Reducing Risks from Mining of Technology Critical Elements
Bibliographic record
Abstract
In recent years, there has been a rapid increase in the global demand for many elements used in green energy and high-tech applications, including antimony (Sb), cobalt (Co), indium (In), lithium (Li), niobium (Nb) and the rare earth elements (REE). Canada has abundant resources of these critical elements, and many companies are working to bring new mines into production. However, we know very little about the potential environmental impacts of mining critical elements compared to mining other commodities such as gold or copper. Processing resources such as REEs has led to environmental degradation in some parts of the world, but there are few published studies of these environmental impacts and related risks to human health. This talk will summarize the key geoenvironmental characteristics of critical element deposits, including results from recent studies of the St. Lawrence Columbium Mine in Oka, Quebec. This mine operated from 1961 to 1976 and at the time was one of the largest Nb producers in the world. Samples of waste rock, tailings, slag, surface water, and groundwater were collected from 2015 to 2018. Mineralogical analyses show that in waste rock and tailings most uranium (U), thorium (Th) and REEs are hosted by low-solubility oxide and silicate minerals. However, some REEs and fluorine (F) are contained in minerals such as calcite and fluorapatite, which are relatively soluble under weathering conditions. Smelter slag at the mine contains concentrations of U, Th and radioactive isotopes that exceed Canadian guidelines for the disposal of radioactive waste. Measurements of U, Th, F, Nb, REEs, and radionuclides in tailings seepage and pit lake waters indicate that these elements are relatively immobile in oxic surface waters, but may be transported in deeper, anoxic groundwaters. The results of this research should help to improve environmental predictions for future Nb- and REE-mining projects and support the development of new environmental guidelines.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".