An ad-hoc network based framework for monitoring brain function
Bibliographic record
Abstract
Ad-hoc networks and mobile devices have become a crucial part of our daily lives. The low cost of wireless devices and free use of ad-hoc networks open an unlimited horizon to create new applications. Moreover, integrating several technologies can achieve almost unthinkable solutions. This paper presents a mobile solution framework to monitor human brain functions during real-life activities. The framework utilizes the internet, GSM wireless networks, Bluetooth technology and a number of data protocols, and consists of three main parts: a Bluetooth portable near-infrared light sensor; a personal digital assistant (PDA) and a personal computer (PC). The real-time data acquisition is performed by the sensor while mobility is provided by the GSM PDA. The data is sent over a various-protocol stack until it reaches the final destination (the host PC). The system provides a powerful light-weight human-brain-function monitoring system in real-life situations outside a lab environment. Several software components have been developed to achieve the integration of all these technologies and devices.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".