Bibliographic record
Abstract
In January, the company reported that production of TiO[subscript]2 slag fell 25% in 2015 versus 2014, as the Anglo-Australian miner continued to reduce TiO[subscript]2 production in Q4 to 223,000 tonnes, 8% below Q3 2015 and 30% below Q4 2014. This brought total volumes of the pigment material for 2015 to 1.089m tonnes, in line with market guidance, but down from 1.44m tonnes produced over the full year 2014. Recent discussions at Rio have centred on making its existing TiO[subscript]2 production more profitable. Over the past few months, we have stepped up our efforts to create a plan that will ensure the mine's long-term operations in a highly competitive global market, the spokesperson told IM in relation to Sorel-Tracey. The company said it has seen indications of market recovery in pigment and paints, however global TiO[subscript]2 inventories remain high. Coupled with lower investments from the oil industry, resulting in declining round billet production at Rio's steel plant, 2016 is anticipated to look much like 2015, which was a
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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 | 0.001 | 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) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".