Bibliographic record
Abstract
Some of the most eye-catching events of 2015 included the bankruptcy of US rare earths miner, Molycorp Inc., the splitting up of global chemicals giant, DuPont, a litany of scandals for Chile's Sociedad Quimica y Minera (SQM) and a number of failed merger attempts, including Potash Corp. of Saskatchewan (PotashCorp) and K+S AG in potash, Iluka Resources Ltd and Kenmare Resources Ltd in mineral sands, Saint-Gobain SA and Sika AG in speciality chemicals, and Halliburton Inc. and Baker Hughes Inc. in oilfield minerals. Away from dwelling the past, this first 2016 issue of IM also has a forward-looking angle, in the form of an article by Vladislav Vorotnikov, IM Correspondent, discussing how Russian embargo on imports of Western foods could benefit the country's mothballed zeolite industry ( pp30-32 ). For this month's Refractories Hotline, Liz Gyekye, Chief Reporter, spoke to leading Brazilian refractories producer, Magnesita Refratarios SA, about how end user demands for quality, efficiency and value for money are setting demanding but achievable goals for the most agile and technically capable businesses in the in the sector ( pp28-29 ).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".