Deep Saline Aquifers as an Emerging Resource for Direct Lithium Extraction (DLE): Learnings from Alberta, Canada
Bibliographic record
Abstract
Summary Deep saline aquifers are an emerging resource for Direct Lithium Extraction (DLE) and are economically competitive and environmentally responsible alternatives to hard-rock and salar style lithium resources. This presentation will provide an overview of grass-roots lithium development, with a focus on the origin of elevated lithium with the Leduc Formation of Alberta, Canada and how a strong understanding of reservoir characterization can be paired with surface technology, including DLE, to become a promising and readily available supply of lithium. This is particularly important in North America, which has few traditional lithium ore deposits, and is seeking to establish a domestic supply. A study of a lithium brine resource in Alberta Canada is presented to demonstrate the evolution of an emerging lithium brine resource from the exploration stages and understanding lithium origins within the subsurface, through to the development and scaling DLE from the laboratory to a field pilot, to plans for a commercial scale processing facility. The study will provide an overview discussion of the technical challenges encountered on both the subsurface and surface sides, as well as the business considerations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".