Bibliographic record
Abstract
Introduction A wireless ad hoc network is an autonomous, self-organized, distributed, peer-to-peer network of fixed, nomadic, or mobile users that communicate over bandwidth-constrained wireless links. There is no preexisting infrastructure. Wireless ad hoc networks can be relay, mesh, or star networks comprising special cases. When the nodes are connected in mesh topology, the network is also known as a wireless mesh network . It is known as a mobile ad hoc network (MANET) , when the nodes are mobile. A wireless ad hoc network has a mutlihop relaying of packets, as shown in Fig. 22.1. It can be easily and rapidly deployed, and expensive infrastructures can be avoided. Without an infrastructure, the nodes handle the necessary control and networking tasks by themselves, generally by distributed control. Typical applications are wireless PANs for emergency operations, civilian, and military use. The wireless ad hoc network is playing an increasing role in wireless networks, and wireless ad hoc networking mode has been or is being standardized in most IEEE families of wireless networks. MANETs and wireless sensor networks (WSNs) are the two major types of wireless ad hoc networks. They both are distributed, multi-hop systems. A MANET is an autonomous collection of mobile routers (and associated hosts) connected by wireless links. Each node is an information appliance, such as a personal digital assistant (PDA), equipped with a radio transceiver. The nodes are fully mobile.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.044 |
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".