Road Importance Using Complex-Networks, Graph Reduction & Interpolation
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
Most people spend hours on the road on a daily basis making road networks a crucial part of our daily lives. Trips to work, grocery store, hospital or even casual jogs and road trips mainly occur on walkable or drivable roads. With the increase of online communities, professionals and enthusiasts, road networks are now abundantly available from various sources making them a great resource for a variety of analysis such as finding the road importance, road characteristics, city planning, and the association between neighborhoods' walkability and the local obesity rate. However, as data increases, analyzing larger regions requires much more processing power and computational time. We aim to incorporate graph reduction and centrality interpolation while utilizing some already-efficient complex networks centrality algorithms, to produce ready-to-analyze road scores for the entire given data set while reducing the required computational time when compared to the conventional algorithms that do not use reduction. Furthermore, our produced road scores can be applied to non-network characteristics such as amenities, elevation, road type, road condition and road structure to produce more accurate walkability scores.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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