Bibliographic record
Abstract
AkzoNobel India's revenues rise; TOR Minerals' profit surge; Chemours: global demand outpacing economy AkzoNobel's India reported rising revenues thanks to increased coating sales, but profits fell in Q2, as input prices rose. AkzoNobel India reported revenues up 1.6%, at Indian rupees (Inr) 7.91bn ($123.15m), thanks to a 2.5% boost in revenues from its coating segment. But rising material costs hit profits, as EBITDA tumbled 32% to Inr 665.5m. Jayakumar Krishnaswamy, managing director of AkzoNobel India, said quarter's performance has been impacted due to slow sales growth and higher input prices. Meanwhile, US speciality materials manufacturer TOR Minerals International Inc. saw profits triple in the second quarter of the year on the back of strong activity in its alumina division, while TiO2 pigment sales were flat. The company reported a net profit of $352,000 in Q2 against $87,000 at this time last year - a growth of over 300%. Revenues rose 9% in the period, mainly as speciality alumina demand increased in a number of markets, primarily Europe.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.026 |
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".