Bibliographic record
Abstract
We have assembled extensive information on the cycles of seven industrial metals in 49 countries, territories, or groups of countries, drawn from a database of some 200,000 material flows, and have devised analytical approaches to treat the suite of metals as composing an approach to a national "materials metabolism." We demonstrate that in some of the more developed countries, per capita metal use is more than 10 times the global average. Additionally, countries that use more than the per capita world average of any metal do so for all metals, and vice versa, and countries that are above global average rates of use are very likely to be above global average rates at all stages of metal life cycles from fabrication onward. We show that all countries are strongly dependent on international trade to supply the spectrum of nonrenewable resources that modern technology requires, regardless of their level of development. We also find that the rate of use of the spectrum of metals stock is highly correlated to per capita gross domestic product, as well as to the Human Development Index and the Global Competitiveness Innovation Index. The implication is that as wealth and technology increase in developing countries, strong demand will be created not for a few key resources, but across the entire spectrum of the industrial metals. Long-term metal demand can be estimated given gross domestic product projections; the results suggest overall metal flow into use in 2050 of 5-10 times today's level should supplies permit.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".