Quality assessment of fresh meat cuts as a performance indicator of knives specifically adapted for robot-assisted operations
Bibliographic record
Abstract
Manual labour in slaughterhouses is hazardous work. Workers suffer from injuries and occupational illnesses resulting from repetitive movements with sharp knives. There is a need for a robotic tool which can perform versatile tasks with a high level of precision. This knife must be able to imitate the same primary cuttings of a professional butcher and produce meat products which are acceptable to the end-market. This paper reports the results of a world-wide assessment of the fresh pork meat cuts as a performance indicator of knives specifically adapted for automated operation. These knives included Victorinox knife, bespoke double bladed Uddeholm knife, vibrating knife and novel smart knife with built-in sensing mechanism that detects in real time the contact with meat and cut depth. Physical appearances of cuts were assessed anonymously by independent responders with different backgrounds. All knives were deemed acceptable in terms of cutting quality. There was also no discernible difference of opinion between manual and robot cuts. This indicates that the new knife for robot-assisted cutting is acceptable by market.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".