Bibliographic record
Abstract
Heading into 2014, the implications of the flooding of Uralkali OAO's Solikamsk II mine in Russia were among the chief supply concerns facing the potash industry. Solikamsk II represented a fifth of Uralkali's capacity, and, by the end of 2015, the company had not been able to recover the mine, having pulled equipment out of the site in February. Also making the headlines for negative reasons in 2015 was Sociedad Quimica y Minera (SQM). Its CEO, Patricio Contesse, was ousted following a number of revelations about the company's business dealings in 2014 and 2015. SQM's potash operations remain relatively stable, but ructions over the company's lithium and iodine businesses posed question marks over its medium-term cash flow. Possibly the biggest story of the year was the attempted takeover of Germany's K+S AG by Canada's Potash Corp. of Saskatchewan Inc. (PotashCorp). Delivered to K+S executives in June 2015, PotashCorp initially offered a EUR 41/share ($45.19/share*) deal. This was rejected by the German company's management in July. PotashCorp withdrew its bid in October after repeated attempts to woo the company, stating that market conditions for the purchase had deteriorated, despite saying only weeks before that the deal was even more attractive than when it was originally proposed.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".