RC Baja Steering and Suspension
Bibliographic record
Abstract
The engineering objective of this project are designing, manufacturing, and testing the most efficient and strongest possible RC Baja Steering and Suspension system that the engineer could produce with the provided or acquired equipment, and materials. This was all done successfully over the school year. During the Fall quarter, the RC car was undergoing designing, and in these design processes, mechanics of materials, statics, and dynamics, were used to come up with the most adequate materials and design. Computer aided designed (CAD) models were then created to get a RC Baja CAD assembly. Winter Quarter of the school year was the manufacturing, and construction process of each and individual part for the RC car. Spring quarter of the RC Car was testing of the entire car to confirm whether the car satisfies the requirements stated in the beginning of the quarter or not. In the suspension components, the front and rear suspension was to have a 2” articulation. Along with this, the car was listed to have a usable 1” of suspension travel front and rear under its own static weight. It was also noted that that the car needed to make a 180 degree turn in a 3.5’ radius, and the car completed this in only a 2’ radius, almost a 60% tighter turning radius. After all the research was done, the car met all requirements. Each part of the car met or exceeded the initial requirements made by the engineer.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".