Detection of foreign matter in beer using an inline foam analysis system.
Bibliographic record
Abstract
In capped beer bottles, CO 2 dissociation during pasteurization is increased with the presence in the bottle of foreign matter such as glass inclusion and visible organic material. During the past 30 years, Canadian operating practices have called for the use of foampicker, people trained to inspect 100% of the bottled beer in order to recognize the associate foam collar anomalies with the presence of foreign matter. This has been regarded as a very successful, albeit expensive, quality control process. When Akitek set out to develop a foreign matter detection system for capped beer bottles, it needed to determine if the foam collar effects were predictable, over a wide range of beer types, and if they could be recognized at an acceptable level of accuracy and repeatability, at line speeds. In order to succeed, Akitek also felt that it was important to incorporate artificial intelligence in the system's design to deal with the complex environment of process, product and container variations and drifts. Furthermore, software and hardware components had to be selected which allow easy adaptation to a wide variety of beer and bottle types and which are insensitive to extraneous noises. This technical paper describes the technologies used, the system development and the results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".