Supply problems are multiplying in the copper market
Bibliographic record
Abstract
Significance Sentiment soured due to rising interest rates, mounting fears of global recession and paltry growth in China’s main copper-consuming sectors. These trends overwhelmed a market that is facing fundamentally undersupplied conditions just as scrutiny of mining operations adds to higher cost of capital. Sentiment is now recovering. Impacts China’s CMOC, operator of Tenke mine in the Democratic Republic of the Congo, is in dispute over export permits, posing risks to output. Bilateral tensions will rise; Panama’s government is demanding USD375mn in taxes and royalties from Canada’s copper producer First Quantum. Efforts to increase supply are rising: Indonesia’s government has warned miners to speed up smelter development or face some export bans. Quellaveco copper operation in Peru, owned by Anglo American and Mitsubishi, is beginning to produce its first copper concentrate. Brazil’s Vale will separate its base metal assets from its flagship iron ore operations and unveil a strategic partner during 2023.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".