Wearable Sensor-Enabled Meat Packaging For Freshness Monitoring and Assurance
Bibliographic record
Abstract
This paper presents a wearable sensor-enabled lab-made packaging (donated as a prototype) for monitoring meat freshness. It highlights the evaluation of printed and wearable sensors created using advanced printing methods. With superior performance and durability, these sensors precisely detect hydrogen sulfide (H2S) gas in part per billion (ppb) levels, temperature (Temp) between −20°C and 80°C, and relative humidity (RH) between 10% and 90%. Thorough testing on fresh and expired meat products validates the effectiveness of these sensors, emphasizing the importance of comprehensive evaluations across different meat types and environmental conditions. Integrating these state-of-the-art sensors into packaging systems facilitates real-time and non-invasive monitoring, enhancing decision-making processes and elevating food quality assurance standards. Our commitment to sustainability drives innovation, fostering a more resilient and environmentally conscious future in the meat supply chain. These advancements aim to revolutionize food safety practices and inspire industry-wide transformation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".