Multi-year in situ hydrogeochemical monitoring of hard rock lithium mine tailings in a large-scale experimental pile
Bibliographic record
Abstract
Spodumene, a lithian mineral found in granitic pegmatites, is a major source of lithium. In situ, multi-year and large-scale (>10s of tons) predictive hydrogeochemical studies can be of great value for informing mine waste rock and tailings management, not least because the material is exposed to the actual climate where it is meant to be stored. However, such studies are rare given their cost and size, and previous ones have focused mainly on sulfide-bearing materials. An experimental field cell filled with spodumene beneficiation tailings was built at the Whabouchi lithium mine site in northern Québec (Canada) and monitored during four consecutive years. In parallel, column laboratory kinetic testing on the same material was conducted for water quality parameters to compare the effect of testing scales on the geochemical behavior. Geochemical results and release rates were overall similar between laboratory and field, and consistent with previously published laboratory results for materials from the same site. The leachates were neutral to slightly alkaline, with ions from feldspars (Ca > Na > K) and residual spodumene (Li) being notable solutes. Concentrations for most solutes surveyed remained higher in the field after four years than in columns at the end of the experiment. One major difference between laboratory and field was a brief (less than 2 years) increase in iron concentrations from the field cell, resulting in Fe-oxyhydroxides precipitation, that was not observed in laboratory. The source of this iron is arguably residual ferrosilicon (used for dense medium separation) and this difference is attributed to the different testing conditions and configurations in the laboratory versus in situ. Field hydrogeological results highlight strong seasonal patterns and the rapid response of this sand-like, sulfide-poor material to ambient temperature changes and wetting-drying events. The tailings both wet and drain rapidly and easily given their water retention curve and the range of matrix suctions recorded during frost-free months. Net infiltration through the tailings was estimated to represent 55% of total precipitation in summer (June-October) 2021. This study provides a comprehensive assessment of the environmental behavior of hard rock lithium mine tailings under real, sub-arctic climatic conditions and outlines similarities and differences between laboratory- and field-generated geochemical results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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 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".