Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Advances in Wireless Body Area Network (WBAN) will allow a range of medical applications that will significantly improve the quality of health care. Placing a number of tiny wireless sensors, on the human body, create a wireless body area network that can monitor various vital signs, providing feedback to the user and medical personnel, a thing that promise to revolutionize health monitoring. Nevertheless the potential of using a body area network with several sensors to monitor vital functions of a human body can only be tapped if we achieve the ease of use and the ease of configuration. In this paper we propose a service-oriented middleware design for WBAN middleware. In the proposed architecture, sensors are coordinated by a gateway node, which in turn retransmits data to a remote central unit and receives WBAN control information and queries from this central unit. The central unit on the other hand will be in charge of storing sensors data, sensor reconfiguration and resource management client, detecting alarms and sending the patients' information to the medical staff. The target user is a patient who needs regular monitoring. The patient usually resides in a care unit or residence for elder people. WBAN in this case shall increase patient comfort and reduces periodical checkups allowing remote monitoring. We believe that the use of Web Services and standardizing the messages exchanged is a potential solution for interoperability and ease of use and configuration challenges. This will attract a larger pool of application developers, leading to more innovative applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it