Bibliographic record
Abstract
DuPont, another major player in the pigment industry, noted that though sales of TiO[subscript]2 had increased slightly in the second quarter of 2014, TiO[subscript]2 prices remained about 3% lower y-o-y. It added that, currently, industry demand is staying relatively stable but that it expects moderately lower ore costs for TiO[subscript]2. While new TiO[subscript]2 feedstock projects are in development, existing feedstock producers have slowed down capacity expansions and even taken existing capacity offline while lacklustre demand for TiO[subscript]2 continues. In 2013, Rio Tinto Minerals announced that it would cancel its feedstock expansion plans while deciding to put its South zircon and rutile processing operations on care and maintenance. The company's Quebec chloride slag production facility was taken offline and the pre-feasibility study was also cancelled in response to weak demand and to reduce operating costs. An uptick in TiO[subscript]2 demand in 2014, owing particularly to a revival in paint demand in the Northern Hemisphere, has led to increases in production by leading pigment producers, Tronox, Huntsman and Kronos, with slight increases in DuPont's TiO[subscript]2 volumes. Some producers have also noted signs of demand recovery in minor markets.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".