Anheuser-Busch InBev reports Third Quarter and Nine Months 2014 Results Highlights
Bibliographic record
Abstract
Except where otherwise stated, the comments below are based on organic figures and refer to 3Q14 and 9M14 versus the same period of last year. For important notes and disclaimers please refer to page 13 Revenue growth: Revenue grew by 2.3 % in 3Q14 and by 5.3 % in 9M14, with revenue per hl growth of 5.0 % in 3Q14 and 4.5 % in 9M14. On a constant geographic basis, revenue per hl grew by 4.9 % in 3Q14 and by 5.2 % in 9M14 Volume performance: Total volumes in 3Q14 declined by 2.6%, with own beer volumes decreasing by 2.7%, while non-beer volumes declined by 0.9%. o US beer sales-to-wholesalers (STWs) declined by 3.7%, with selling-day adjusted sales-to-retailers (STRs) declining by 1.9% o Volumes in Mexico grew by 2.9%, with strong performances by Corona, Bud Light and Victoria o Beer volumes in Brazil grew by 0.2%, being impacted by a soft consumer environment o Volumes in China declined by 4.9%, mainly due to cold temperatures in August and September. In 9M14, total volumes grew by 0.8%, with own beer volumes up 0.6 % and non-beer volumes up 2.6% Focus Brands: Volumes of our global brands grew by 3.1 % in 3Q14, led by global Corona which grew by 6.7%, and global Budweiser which grew by 2.8%. Our total Focus Brands volumes declined by 1.0%
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.564 | 0.433 |
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".