regarding the duties of issuers of financial instruments which have been admitted for trading on a regulated market.
Bibliographic record
Abstract
Except where otherwise stated, the comments below are based on organic growth figures and refer to 3Q13 and 9M13 versus the same period of last year. For important disclaimers please refer to pages 2/3. HIGHLIGHTS Revenue growth: Revenue grew by 3.0 % in 3Q13 and by 2.8 % in 9M13, with revenue per hl growth of 4.2 % in 3Q13 and 5.1 % in 9M13. On a constant geographic basis (i.e. eliminating the impact of faster growth in countries with lower revenue per hl) revenue per hl grew by 4.9 % in 3Q13 and by 5.7 % in 9M13 Volume performance: Total volumes in 3Q13 declined by 1.3%, with own beer volumes decreasing by 1.4%, while non-beer volumes declined by 0.8%. In 9M13, total volumes declined by 2.1%, with own beer volumes down 2.0 % and non-beer volumes down 3.2% Focus Brands: Our Focus Brands volumes grew 0.3 % in 3Q13, with our global brands up 5.0%, led by global Budweiser, which grew by 8.1%. Global volumes (excluding the US) of our new flagship brand Corona grew by 3.7 % in the quarter Cost of Sales: Cost of Sales (CoS) decreased by 1.2 % in 3Q13, and by 0.3 % on a per hl basis, benefiting from synergies captured in Mexico. In 9M13, CoS grew by 1.3%, and by 3.6 % on a per hl basis. On a
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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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.019 | 0.010 |
| Insufficient payload (model declined to judge) | 0.150 | 0.117 |
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".