From Prospect to Postclosure: Essential Tailings Management Knowledge for Exploration Geologists
Bibliographic record
Abstract
Editor’s note: This is the last paper in the Geology and Mining series, which has aimed to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. The 29 chapters plus two others have been compiled into a book, sponsored by BHP and edited by Dan Wood and Jeffrey Hedenquist, which is now available Open Access on the SEG store (www.segweb.org/store). It will soon be available on GeoScienceWorld, and a limited print run will produce hard copies for purchase. Abstract Mine tailings, the residual materials from mineral extraction, present one of the mining industry’s most complex environmental and engineering challenges. Comprising finely ground rock and residual chemicals, tailings require meticulous management to prevent ecological harm and ensure public safety. For exploration geologists, understanding this is not a downstream consideration but a fundamental responsibility that begins at discovery. The consequences of mismanagement are stark; since 2010, major tailings dam failures have caused numerous fatalities, contaminated thousands of kilometers of waterways, and triggered billions of dollars in remediation costs. These disasters underscore the critical need for specific planning and risk mitigation starting with the exploration phase to prevent similar outcomes. This paper provides exploration geologists with a comprehensive overview of the tailings management life cycle, covering material characterization, surface and underground disposal methods, risk mitigation strategies, best practices in monitoring and closure, and opportunities with tailings reprocessing. It demonstrates that integrating tailings considerations into the earliest phases of exploration—by informing site selection, characterizing geologic materials, and identifying geohazards—offers the most effective and economical path to minimizing long-term liabilities. By embracing their roles as the first stewards of a project, geologists can lay the foundation for safer, more sustainable mining outcomes.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".