The new norm for gold miners and how change in mine waste management could help
Bibliographic record
Abstract
The gold mining industry has been out of favour with institutional investors in the last decade as major mergers, acquisitions and other shapes of restructuring took place right at the height of the commodity cycle, leaving the gold mining companies laden with debt. At the same time, institutional investors have observed a new trend in heightened emphasis on environmental, social and governance (ESG) related matters. As institutional investors have a lot of influence on gold mining companies through their significant majority shareholding positions, alignment with their ESG investment criteria is critical to gold mining companies for financial sustainability. This paper explores the ways alternative mine tailings management practices could help gold miners align with institutional investors’ ESG criteria. Maximising the application of thickened tailings, paste, filtered tailings, and underground and open pit backfill directly translates into reductions in land use and hazardous waste, increased water efficiency, and potential increases in energy efficiency; hence, reduced GHG emissions. A reduced waste footprint has indirect benefits related to other ESG aspects like biodiversity and potential social impact. The paper provides a qualitative to semi-quantitative assessment of the impact on lifecycle return on investment in these seemingly more elaborate and costly mine waste management approaches by improving ESG scores and enhancing the chances for access to more favoured and affordable financing.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".