Bibliographic record
Abstract
The platinum group elements (PGEs) are crucial for modern technology, including platinum, palladium, rhodium, ruthenium, osmium, and iridium. The PGEs can be found in a variety of mineral deposits, including stratiform systems, norite intrusions, nickel-copper bearing sills, and placer systems. The mining history of PGEs starts from 1788, dominated by placer deposits in Columbia and Russia until the early 1900s when the nickel-copper fields of Sudbury in Canada and Nori’lsk-Talnakh in Russia rose to prominence, followed by the primary PGE deposits of the Bushveld Complex in South Africa from the mid-1900s. This chapter explores the mining history, demand, and geology of the PGEs; synthesizes a comprehensive assessment of reported mineral resources containing PGEs; and briefly explores the most common sustainability issues affecting the PGE sector globally, such as technical factors, tailings management, risks to water resources, and social and economic issues – or sustainable development more broadly – as well as the need for transparent reporting on all environmental-social-governance (ESG) issues. The chapter is a comprehensive but concise review of the global PGE sector, highlighting its challenges and opportunities within a sustainability framework.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.063 |
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".