Cycling modelling under uncontrolled outdoor conditions using a wearable sensor and different meteorological measurement methods
Bibliographic record
Abstract
The aim of this study was to model the cycling displacement under uncontrolled outdoor conditions with a wearable sensor and different meteorological measurement methods. One participant completed eight courses of a distance of 9.2 ± 2.4 km with varied profiles and directions. Data were recorded every second with a power meter, a GPS and a speed sensor. The aerodynamic drag coefficient, measured by a Notio wearable sensor, and the meteorological variables provided by the Notio, a Kestrel fixed meteorological station and the OpenWeather website were integrated into the Martin mathematical model to calculate the theoretical power output. The power calculated by the model on the basis of data from Notio, Kestrel and OpenWeather were, respectively, 1 ± 4 W higher, 7 ± 15 W lower and 67 ± 111 W higher than the power measured by the sensor. The overall RMSE and R 2 , including 7325 data points, were 12.8 W and 0.77 ( p < 0.001), respectively, between the power output measured by the sensor and the power output modelled with the data from Notio. The use of the model with the wearable sensor was more precise mainly due to the relative wind measures at all points of the course. Therefore, the Notio can be useful for coaches to follow the evolution of the C d A of athletes on the field. Moreover, the model has the potential to predict the time of a cyclist just before a time trial in order to optimise his pacing strategy taking into account actual weather conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".