A Review of the Replicability and Implementation of the Efficient Clustering Scheme for MANETs in Remote Canadian Communities
Bibliographic record
Abstract
Remote communities in Canada lack Internet availability and connectivity. With online interactions becoming increasingly necessary in recent years, this lack of availability has furthered the Digital Divide in Canada. Mobile ad hoc Networks (MANETs) are an alternative to traditional networking options that can help community members use digital applications to communicate with one another more reliably. Leveraging clustering in MANETs has the potential to further increase connectivity and reliability for users, while also limiting the overhead of the networks. The Efficient Clustering Scheme (ECS) is one such clustering algorithm, that has the potential to be employed in remote community due to its key properties of clusterguest nodes and lack of Network downtime for restructuring. Unfortunately, due to the lack of reproducibility of MANET research, many untested assumptions are required to employ the ECS, threatening its applicability as its introduction could lead to further issues for the community members. Therefore, this thesis explores the implementation of ECS and attempts to replicate the previous study by simulating how the ECS would operate in an area with a similar density to Rigolet, a remote Canadian community in Nunatsiavut.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".