Expectations weigh heavily for TiO2, but who is protecting the secrets?
Bibliographic record
Abstract
''Stable TiO 2 prices that have prevailed over the past few quarters remained in place in many contracts at the start of 2014. However, buyers did not always accept the roll-over of TiO 2 prices into the new quarter,'' the report explained. [Ellen Kullman] admitted that she had spent ''more time than I ever thought I would need on intellectual property protection,'' adding, ''I think it is a reality (...) It's just not one country, it's just not one science or technology. I think it's something every company faces in this ''TiO 2 is a very tough process. It is as much intellectual property as it is know-how and just because you can build a plant doesn't mean you can run a plant. We are very strong on protecting our intellectual property. We designed the plant specifically for China, given the geography and the access to all of the support facilities one would need, and we did it in a way that enabled us to protect what we were doing there,'' she added.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".