Bibliographic record
Abstract
With optimism on the titanium dioxide (TiO2) scene contrasting slipping zircon prices, Cameron Perks, consultant and IM correspondent, spoke to managing director and CEO of Iluka Resources, David Robb, about the mineral sands market and where the world's most significant zircon producer thinks it may be heading. This industry is in need of new-high quality ore body discoveries, tier one ore bodies. We haven't really seen a significant new discovery since Jacinth Ambrosia in South Australia in 2004. Before that, the industry relied heavily on mineral sands provinces in South Africa, Western Australia and Canada, and we haven't discovered their equal. We think it's necessary for us to continue to explore on a broad front, so that's what we're doing. Ilmenite is not an important product for us. We're a demand follower, we adjust our production to suit the demand that we see. We're very happy with the trends we see in high-grade ores, rutile and synthetic rutile, which are more important to us than ilmenite.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.022 |
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".