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
A mobile ad hoc network is a dynamic mobile wireless network that can be formed without the need for any pre-existing wired or wireless infrastructure. One of the main challenges in an ad hoc network is the design of robust routing algorithms that adapt to the frequent and randomly changing network topology. Organizing mobile nodes into manageable clusters can reduce routing overhead and provide more scalable solutions. In this paper we propose a mobile agent-based clustering architecture for routing in mobile ad hoc networks. Using this clustering architecture, hybrid routing schemes can be employed for intra-cluster and inter-cluster routing to improve the performance of routing. All nodes use two mobile agents to perform routing and clustering operations. They collect routing and clustering information and periodically maintain the corresponding tables. We identify various parameters for improving network performance in the proposed clustering architecture. By using some optimal values for these clustering and routing parameters, more robust and scalable routing can be attained.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| 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