Anheuser-Busch InBev reports Fourth Quarter and Full Year 2014 Results HIGHLIGHTS
Bibliographic record
Abstract
Except where otherwise stated, the comments below are based on organic figures and refer to 4Q14 and FY14 versus the same period of last year. For important notes and disclaimers please refer to page 19 Revenue growth: Revenue grew by 5.9 % in FY14 and by 7.6 % in 4Q14, with revenue per hl growth of 5.3 % in FY14 and 7.6 % in 4Q14. On a constant geographic basis, revenue per hl grew by 5.7 % in FY14 and by 7.4 % in 4Q14 Volume performance: Total volumes grew by 0.6 % in FY14, with own beer volumes growing by 0.5 % and non-beer volumes growing by 1.3%. Total volumes were flat in 4Q14, with own beer volumes growing by 0.2%, while non-beer volumes declined by 1.8% Focus Brands: Volumes of our Focus Brands grew by 2.2 % in FY14 and by 1.3 % in 4Q14. Our global brands grew by 5.4 % in FY14, led by Budweiser which grew by 5.9%, and Corona which grew by 5.8%. Our global brands grew by 4.7 % in 4Q14 Cost of Sales: Cost of Sales (CoS) increased by 2.9 % in FY14 and by 6.6 % in 4Q14. CoS per hl increased by 3.9 % in FY14 and by 7.5 % in 4Q14, on a constant geographic basis. The increase in CoS per hl in 4Q14 was driven primarily by higher depreciation and packaging costs in Brazil, as well as additional bottle costs in Mexico related to higher than expected demand for Corona globally
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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 | 0.004 | 0.014 |
| 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 | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.603 | 0.456 |
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".