Visualization of Professional Cyclists Analytics
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
Abstract Cycling is an important field of sport and a great example of a sport in which athletes are highly measured due to cycling computers that monitor and document workouts in detail. Leveraging this variety of data, we developed The Velodrome, a web-based analytics tool in collaboration with the Israel Premier Tech pro-cycling team to support decision-making. Unlike traditional tools that focus on individual cyclists, The Velodrome enables comparative analysis of multiple cyclists, assisting coaches and directeur sportifs in race selection, strategic preparation, and training decisions. The Velodrome integrates both objective metrics (e.g., relative power, elevation gain) and subjective metrics (e.g., sleep quality, fatigue level) to provide a holistic view of each cyclist’s physical and mental state. The platform offers various visualizations, including radar and line charts, facilitating multi-cyclist and time-based comparisons. These features enable detailed insights into training loads, performance trends, and readiness for competition, supporting team-level decision-making.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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