Low Cost Data Acquisition for Racing Applications
Bibliographic record
Abstract
<div class="htmlview paragraph">In the increasingly complex and expensive arena of motor sport, data acquisition has become an essential tool of today's race engineer. With the limited budget and testing time available to most Formula SAE teams, a more effective data acquisition system, compared to off the shelf models, is needed. Therefore, a wireless data acquisition system was created to help improve the performance of the University of Calgary Formula SAE racecar. Our objective was to design and build a system that enabled the team to test and measure the vehicle performance in real-time. This live system will allow any race team to analyze the data prior to the car entering the pits allowing for appropriate modifications to be made at this time. This greatly reduces the number of pit stops and track time required to arrive at the optimum setup. The system has also been used as a driver aid. This has been accomplished by mounting sensors to the car's chassis, using prototype hardware to transmit and receive the data through a wireless link, and developing software to analyze and record the data.</div>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".