Study on the Application of Biological Tactile in Fast Meat Freshness Detection
Bibliographic record
Abstract
The author aimed to explore the application of biological tactile in fast non-destructive meat freshness detection. Used WDW-20 electronic universal testing machine, surveyed chicken, pork and beef the pressure characteristic curves, analysed respectively the relationship between the pressure characteristic curve parameters and the meat freshness. The author also analysed the relationship between the shape of pressure characteristic curve and the meat freshness. The results indicated that in the meal pressure characteristic curve, the curve shape and a number of mechanical parameters could reflect its freshness. Also, different types meat had different structures led them to the different mechanical properties; different types meat characteristic curves, the meat pressure characteristic curve shape and parameters reflected their fresh meat differently. Biological tactile can evaluate meat freshness in a few seconds. This is a promising, economic, simple and practicable way of fast meat freshness detection. Key words: Biological tactile; TVBN; pressure Characteristic curve
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".