4G LTE User Equipment Measurements along Kingston Transit 502 Bus Route
Bibliographic record
Abstract
The collection and construction of this dataset is part of an exhaustive data collection campaign organized by the Queen's Telecommunications Research Lab (TRL) and lead by Habiba Elsherbiny, a former MSc. student at TRL. The dataset includes several 4G LTE UE-related wireless network parameters logged using Android phones while on the bus. The data was collected along the Kingston Transit Express Bus 502 public bus route in Kingston, Ontario, Canada. To the best of our knowledge, this is the first extensive analysis to be carried out over 4G LTE networks along public transportation in a midsize city like Kingston reflecting the various dynamics of the route. We managed to collect more than 190 thousand unique data points representing 30 trips covering a total of 700 km in over 30 hours. We made the dataset publicly available on Dataverse platform in an effort to help other researchers in the field conduct cellular network analysis. Check the README file for full details.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.015 |
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".