Geochemistry of decades-old spodumene mine tailings under a humid continental climate
Bibliographic record
Abstract
Transports electrification efforts currently increase global demand for lithium, but literature about the environmental geochemistry of spodumene mine tailings is very scarce. Mine tailings from the former Québec Lithium Corporation site in La Corne, Québec, Canada, were submitted to a suite of geochemical and mineralogical characterizations to evaluate metals distribution and mobility 55 years after deposition. From 1955 to 1965, production of spodumene concentrate, lithium carbonate and other lithium salts from a spodumene-pegmatite ore left behind about 750,000 tons of non-sulfidic tailings (mostly from flotation). Core samples of the tailings were collected from 5 boreholes ranging from 5.13 to 9.75 m in depth. Results show that grain size and metals distribution is heterogeneous, with elements groups (Li–Ta–U or K–Rb) being enriched or depleted in coincident strata and zones. The mineralogical assemblage is dominated by quartz, albite and other feldspars, unrecovered spodumene (up to 12 wt%), and micas, displaying few to no signs of weathering. Sequential extraction results indicate that for most elements surveyed, including Li and Be, secondary retention mechanisms like precipitation, sorption, and ion exchange, almost exert no control on their mobility within the tailings. Silicate-hosted elements (Al, Li, Be, K, Rb) were proportionally among the least extractable. The most extractable elements are either ubiquitous in the environment (Fe, Mn) or hosted in metamict minerals (U). Apart from paste pH which tends to be relatively lower close to the surface, no relation is evident between depth or the water table level and any physical or geochemical parameter. Despite the tailings being almost unweathered, the surrounding waters contain Li concentrations up to the mg/L range. This study gives environmentally-relevant insights into the geochemistry of non-sulfidic spodumene-pegmatite mine tailings as surging worldwide demand for Li attracts interest towards new spodumene mining projects.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".