Lessons learned from 20+ years post-closure care of BHP’s legacy mine sites in North America
Bibliographic record
Abstract
In the past, mine closure across the industry was mostly about meeting regulatory compliance with a focus on physical stability of the reclaimed landscape and revegetation of disturbed areas, with a preference for the lowest cost option.Today, BHP is focused on achieving optimised closure outcomes on a fit-for-purpose, site by site basis in consideration of sometimes competing interests such as obligations, corporate values, stakeholder expectations, and cost.It is acknowledged that how some sites were developed and/or closed in the past was not necessarily the best in terms of post-operations life when we apply a modern set of optics.Using hindsight from experiences within BHP's Legacy Assets, this paper presents numerous lessons learned in support of achieving optimised closure outcomes and objectives for mine sites.Key lessons learned include:• Relinquishment is a great aspiration for closed mine sites, but sites should be developed, operated, and closed in the event long-term care and maintenance becomes a reality.• Closure-related decisions should be based on risks, not solely on regulatory compliance.It is acknowledged that most jurisdictions are moving to a risk-based as opposed to a prescriptiveapproach for final closure of sites.In some jurisdictions, however, mine closure regulations are not stringent enough to force owners into a risk-based approach, supported by robust science and thorough technical assessments to select an optimised closure strategy.• Selecting an optimised mine closure strategy should be based on the undiscounted value of estimated closure and post-closure costs.If closure strategies are selected based on present value of these costs, the industry tends to favour strategies that offer the least number of opportunities to build social value while at the same time, leaving the site/owner exposed to higher closure risk due to issues such as changing societal and regulatory demands, climate change, and emerging chemical species of concern.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".