Bibliographic record
Abstract
Optimized Link State Routing protocol, an ad-hoc routing protocol, has been popular in wireless devices running on Linux operating system for quite some time. In this project we have outlined the process of preparing Android devices for ad-hoc networking, a way to overcome limitations of the OS for continuous UDP communication, ensure all devices communicate on the same wireless Wi-Fi SSID, Cell-ID, subnet and finally implement the Optimized Links State Routing (OLSR) in Android Operating System using Google Nexus 7 devices. Using the code base from ProjectSPAN, an open source project, OLSR protocol has been ported to Android Nexus 7 devices. The core application is divided into two major sections, MANET and OLSR. Mobile Ad-hoc Network portion of the code takes care of setting up the device for ad-hoc mode communication, firewall and peripheral setup while OLSR portion of the code maintains the neighbor tables, MPRs and routing. The project also describes the process by which a device is prepared to run low level custom codes in Android operating system. The OLSR implementation has been successfully tested with three nodes test bed, demonstrating the multi-hop ad-hoc networking capabilities of this wireless routing protocol. With the aid of the Android’s graphical interface the application is able to exhibit the dynamic nature of the OLSR protocol. As nodes and neighbors in the network moves around with respect to time and relative location, OLSR protocol is able to form new neighbors and elect Multipoint Relay in real time.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.087 |
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".