Grand challenges in anticipating and responding to critical materials supply risks
Bibliographic record
Abstract
Critical materials are resources that are vulnerable to supply disruptions, where those disruptions can have significant adverse impacts on society. In the coming years, materials supply risks associated with the energy transition and geopolitics are likely to intensify and new risks are expected to emerge. This perspective identifies three "Grand Challenges" that represent frontier areas for critical materials research and highlights some promising new directions for each area: (1) extending visibility downstream to value-added materials beyond elemental forms; (2) quantifying the risks associated with market dynamics; and (3) developing tools to inform policy interventions. Emerging digital capabilities have the potential to play a significant role addressing long-standing limitations in data quality and access to unlock progress on these challenges. Progress in these areas can equip decision-makers across industry, government, and finance with tools to understand the complexity and uncertainty introduced by these real-world challenges.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".