Bibliographic record
Abstract
New sources of talc are now being offered from Pakistan and North Korea. While Pakistan is credited with much of the new talc in the market, the majority of it comes from Afghanistan, where high quality white talc is being exported via Pakistan to all continents, with some material even going to China. Global demand for talc is expected to rise steadily, with strong growth in plastics, coatings and technical ceramic markets, offset by a fall in consumption in paper and traditional ceramic applications.Talc production in China remains stable around the 1.9m tpa mark. India continues to develop as a supplier, meanwhile, and is now the second largest producing country. A decline in the use of talc as a paper filler has been more than offset by growth in the use of talc in polymers, especially for automobile parts. Mineral commodity specialist Bob Virta reports that US Geological Survey (USGS) draft estimates, yet to be finalised, for global production of talc and pyrophyllite in 2014 were just over 7.458m tonnes, with 6.304m tonnes for talc and 1.154m tonnes of pyrophyllite. The estimated talc production for 34 countries in 2014 is shown in Table 3. The USGS' estimate for Chinese production in 2014 is 2.2m tonnes, but this has been reduced to 1.9m tonnes in line with other estimates. Afghanistan was not included in USGS estimates, so 400,000 tonnes have been added, taking overall global talc production to 6.4m tonnes. Imports of talc into the US were 242,000 tonnes in 2010, 285,000 tonnes in 2011, 350,000 tonnes in 2012, 269,000 tonnes in 2013 and 260,000 tonnes in 2014. The peak of 350,000 tonnes came in 2012 and although exact sources are not known for this year, if it is assumed that sources and proportions were the same as in 2014, the import tonnages would have been 105,000 tonnes from China, 101,500 tonnes from Canada, 80,500 tonnes from Pakistan and 63,000 tonnes from other countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".