Bibliographic record
Abstract
According to Joseph D. Bruhin, chief information officer of Constellation Brands, experiments with graphic presentation of data are making it easier for sales people to see how they're performing right in the field. Constellation Brands, a distributor of beer and liquor, is a publicly traded S&P 500 Index and Fortune 1000 company, with 2015 net sales of approximately $6 billion and 7,600 employees. It is headquartered in Victor, New York, with operations in the United States, Canada, New Zealand, Italy, and Mexico. Joseph D. Bruhin is Constellation's senior vice president and chief information officer. Bruhin's role includes developing new tools for salespeople to use in the field and new measurements for understanding how products are performing at retail. He has been particularly focused on making data easy to analyze and understand. In an interview, Bruhin details the particular challenges faced by companies in the beverage alcohol business and the role that graphics are playing in helping employees more easily track sales against metrics.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".