An ad-hoc network based framework for monitoring brain function
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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