Heavy Metal: Earth’s Minerals and the Future of Sustainable Societies (PDF)
Bibliographic record
Abstract
Heavy Metal: Earth’s Minerals and the Future of Sustainable Societies brings together world-leading experts from across the globe to reimagine the future of mineral exploration and mining in a post-fossil fuel world. Minerals and metals – for batteries, circuit boards, wiring and other components – are essential to a digital, carbon-neutral economy. But how can we grapple with the environmental, social and geopolitical challenges caused by the extraction and use of these critical resources? Concise, accessible, and engaging, the essays in this timely collection intertwine a broad spectrum of disciplines to help us understand and reimagine our relationship with minerals. Exploring a wide range of themes, from the colonial history of mining and Indigenous resistance, to new frontiers in exploration geology, waste management and recycling, this book draws on experts from fields as diverse as geology, mining engineering, law, economics and public policy. The book also explores mineral resources through an artistic lens, with a collection of stunning images from the Canadian photographer Edward Burtynsky, and excerpts of a new musical work, the Heavy Metal Suite. This thought-provoking and ultimately hopeful book guides us towards a more responsible, ethical and sustainable use of metals and minerals. It is essential reading for anyone interested in how we supply the resources needed for a carbon-neutral economic future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.057 |
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".