Anthropogenic Nickel Cycle: Insights into Use, Trade, and Recycling
Bibliographic record
Abstract
The anthropogenic nickel cycle for the year 2000 was analyzed using a material flow analysis at multiple levels: 52 countries, territories, or country groups, eight regions, and the planet. Special attention was given to the trade in nickel-containing products at different stages of the cycle. A new circular diagram highlights process connections, the role and potential of recycling, and the relevance of trade at different life stages. The following results were achieved. (1) The nickel cycle is dominated by six countries or territories: USA, China and Hong Kong, Japan, Germany, Taiwan, and South Korea; only China also mines some of its nickel used. (2) Nickel is mostly used in alloyed form in stainless steels (68%). (3) More scrap is used for the production of stainless steels (42%) than for other first uses (11%). (4) Industrial machinery is the largest end use category for nickel (25%), followed by buildings and infrastructure (21%) and transportation (20%). (5) 57% of discarded nickel is recycled within the nickel and stainless steel industries, and 14% is lost to other metal markets where nickel is an unwanted constituent of carbon steel and copper alloy scrap.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".