Bibliographic record
Abstract
Imerys attributed the current, inclusive revenue increase partly to group structure effect, referring to the integration since 1 March 2015 of Greek miner S&B and bolt-on acquisitions in monolithic refractories and carbonates, which it said added around EUR 238.3m to its sales. A positive exchange rate effect of EUR 188.1m, related to the decline of the euro against the US dollar in particular, also boosted revenue. The performances achieved to the end of September show that external growth is earnings-enhancing and that S&B's integration has started to deliver the expected synergies, [Gilles Michel] said. Revenue for Imerys' energy solutions and specialities segment, which includes its proppants business, dropped by 6% on a like-for-like basis so far this year to EUR 950.2m, from EUR 963.1m last year. For Q3, the decline was even sharper, at 9.7%, to EUR 314.1m from EUR 338.9m a year ago. High resistance materials, which covers products Imerys sells mainly into steel, glass, foundry and aluminium markets, recorded a 7.2% drop in like-for-like revenue and a 0.6% rise in current revenue for the first three quarters of 2015 to EUR 486.2m, from EUR 483.1m last year. Third quarter revenue fell 3.4% like-for-like but increased 1.1% on a current basis to EUR 156m from EUR 154.2m a year ago.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.064 |
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".