Bibliographic record
Abstract
Abstract The platinum‐group metals, ie, platinum, palladium, rhodium, ruthenium, osmium, and iridium, all have high melting points and exceptional corrosion resistance. Platinum, rhodium, and iridium are particularly stable to oxidation at high temperatures. These metals, especially platinum and palladium, show exceptional catalytic properties. In nature, platinum‐group metals frequently occur in conjunction with nickel, copper, and iron sulfides. The principal producing region is South Africa, but other commercially important deposits exist in Canada and CIS. In the refining of these metals, solvent extraction technology is increasingly replacing conventional hydrometallurgical processes. Benefits include increased recovery, improved efficiency, and significantly shortened process time. Resources and recovery processes are discussed. The platinum‐group metals are extensively used as catalysts. Applications include pollution control catalysts for gasoline and diesel vehicles, control of emissions of organics and nitrous oxides from industrial installations, fuel cells and process catalysts in the chemical industry, including nitric acid and chlorine production, and petroleum organic synthesis refining. Platinum‐group metals are also used as jewelry, in the glass industry, in electronic components, in spark plugs, on turbine blades, and in medical devices. Modern cardiovascular surgery and anticancer chemotherapy both employ these metals.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.051 |
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".