Bibliographic record
Abstract
In future wireless networks, Mobile IP will be widely deployed as a general mobility protocol. Currently, in theprotocol each mobile node (MN) should have one public home address to identify itself when it is away from home. Unlike the stationary host, the MN cannot simply use private addresses when NAT (Network Address Translation) is enabled. How to assign public addresses among mobile nodes is important to save the already limited IPv4 addresses. Even though Mobile IPv6 can provide a large address space, when communicating with IPv4 based hosts, the MN still needs to use one public IPv4 address. Protocol translation can map between IPv6 and IPv4 addresses;however, it is a NAT-based approach and breaks end-to-endcommunications. From a new perspective, we propose anaddress-sharing mechanism that allows a large number of MNs to share only one IPv4 public address while avoiding most of the drawbacks of NAT.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.021 |
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".