A novel ubiquitous system to monitor medicinal cold chains in transportation
Bibliographic record
Abstract
Cold chain is a term related to the equipment and processes used to keep the correct temperature, in which the products, such as food, vaccines, blood, tissues, amongst others, should be stable to be preserved. Any change in temperature can cause a damage in the specific properties in products. Because of that, it is mandatory to constantly monitor temperature and log it to offer traceability. Furthermore, if products must be transported, the position coordinates should be taken into account as well, due to the possibility of making mistakes in logistics personnel, taking non-optimal routes to arrive to the destination, and increasing transportation time. Thus, logistics managers need tools to measure, save and analyze temperature and position in real time to make the most optimal decisions. The implementation of systems meeting Ubiquitous Computing can fulfill the challenge, because the generated information is available to be read, modified and stored everywhere and every time. Besides, messengers can be warned about anomalies regarding change of temperatures or coordinates, adding context awareness to the system. This work aims to show a novel architecture to monitor cold chains by using Ubiquitous Computing paradigm, by means of Single Board Computers. The system includes instrumentation, embedded processing with Single Board Computers, real time databases, Human Computer Interfaces, remote management and support to deploy a complete solution. By using this system, companies ensure traceability and integrity of data in cold chains. A study case is presented to validate the approach. © 2017 AISTI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".