Mining Indaba '16: : We've yet to hit rock bottom, says Baker & McKenzie
Bibliographic record
Abstract
Nobody wants to be the one who gives ground, as that may be letting their competitors in, one attendee at this year's Mining Indaba told IM. The problem is, this strategy means running a company into the ground before giving up, to a point where nobody will want to buy or revive it in the future. Since then, prices have not rebounded to more than $300/tonne. prices have not recovered to reach the $400/tonne mark (FOB Vancouver), [Smith] said. But at $240/tonne, believe that the market had bottomed out. Potash is an elastic commodity and consumption is sensitive to price. In general, for every $50/tonne increase in potash prices, we see demand decline by 1.1m tonnes. Overall, growth in potash demand is expected to be moderate compared to supply, Smith said. I expect consumption to reach 56.9m tonnes by 2020, leaving the market with an overall higher surplus than currently. In an oversupplied market, only those producers that have long-term supply agreements in place are expected to be successful, Smith said.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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".