Surging zinc price remains vulnerable to a correction
Bibliographic record
Abstract
Subject Zinc market Significance The zinc price rose by more than 50% last year -- the best performance of all base metals traded on the London Metal Exchange (LME). After approaching a ten-year high of 2,900 dollars per tonne in November, the price remains sharply higher than its low of 1,500 a year ago. Mine closures in Australia and Ireland removed 1.1 million tonnes of zinc from the market, limiting the increase in output last year to an estimated 0.5%. In contrast, demand rose strongly, led by Chinese infrastructure spending, which accounts for around one-quarter of zinc demand. The 2016 deficit was estimated at 400,000-600,000 tonnes, the fifth year of shortfalls. Impacts Northern Chinese smelters are increasingly turning to North Korea for zinc concentrate, making the country China's third-largest supplier. Namibia's Skorpion mine may close two years earlier than forecast, removing an estimated 140,000 tonnes of refined metal from the market. South Africa's Gamsberg mine, one of the world's largest undeveloped zinc deposits, is due to begin production in 2018. Rising prices are attracting more buyers for the mines that Belgium-based producer Nyrstar has for sale under its restructuring programme.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.087 | 0.047 |
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".