Bibliographic record
Abstract
This paper presents BikeVibes1, an app that cyclists can use to log data regarding the smoothness of their rides. The main goal of BikeVibes is to facilitate the collection of anonymized open data about road quality that others can download and peruse. A few sample scenarios where having this type of crowdsourced data would be useful are as follows. A city can use the gathered data in order to determine which roads need to be maintained/upgraded since the quality of the road can be perceived very differently when riding a bike compared to driving a car. Likewise, a city can determine paths that are more frequently used by cyclists in order to decide where to build or upgrade dedicated bike lanes and/or how to prioritize maintenance. Also, third-party app developers can use the road quality data to suggest paths to cyclists based on smoothness, as this may be an important attribute for some people, e.g., in the case of parents riding bicycles hauling trailers with children. None of these scenarios could be easily contemplated without the availability of data such as that gathered through BikeVibes.
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 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".