Bibliographic record
Abstract
Despite the implementation of product price rises, the company has been struggling with rising raw material costs with headwinds expected to continue in 2018. The higher cost of raw materials such as titanium dioxide and a fall in shipments of marine and oil and gas coatings hit AkzoNobel’s first-quarter revenues, it said. Although the average selling price for its coatings products was up by 3% year on year in the first three months of 2018, revenues fell 8% to €2.16 billion ($2.64 billion) thanks to currency effects and a 3% drop in volumes. “Volumes in marine coatings continued to be affected by the slowdown in new-build activity, despite some recovery in other segments,” AkzoNobel said. “Protective coatings volumes decreased due to fewer oil & gas projects.” The company’s net income from continuing operations fell by 6% - the high price of raw materials, such as titanium dioxide, continued to weigh on profits. “Headwinds experienced during 2017 - including higher raw materials costs and adverse effects from foreign currencies - are projected to continue in 2018, especially [at] the start of the year,” AkzoNobel said.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".