Bibliographic record
Abstract
Co extrusion sausage technology has been a game changer in the industry as it has transformed the traditional way of filling meat betters into premade casings (e.g., synthetic materials such as cellulose or natural casings) to producing the casings on top of the meat batter as it comes out of the stuffer. This allows full automation of the process (working 24/7) with lower waste, while also increasing food safety standards. The main material used is collagen which today is extracted from a limited type of beef hides. It is then treated with a strong alkaline solution, to breakdown some of the original structures, and later with a strong acid to allow swelling of the resulting suspension. The application of the collagen gel requires special equipment to line up the collagen fibers in such a way that they provide the strength and elasticity during the cooking operation (meat expends during cooking) and later provide the consumer with the characteristics bit/“snap” similar to natural casings. Understanding the interactions between the chemical and physical properties of collagen is essential in obtaining good results. During production it is important that the collagen is quickly dried and crosslinked so the shape of the product will not be modified. This requires special large horizontal dryers and a unit to apply liquid smoke (source of aldehydes for crosslinking). Currently most systems are built for large capacity production, and therefore require large investment, however the industry is working on developing smaller and more flexible units for future use.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".