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
The ASME RC Baja is a competition that tests engineering students’ ability design, build, and test an RC car. The vehicle then competes against other engineering student’s RC cars. The objective is to manufacture the most efficient and cost-effective RC Baja car. During the first quarter of senior project, the student’ engineering skill was applied to designing an RC car that will be able to compete in a competition at the end of spring quarter. The student was tasked with using all the information from the classes the student had taken previously at Central Washington University and design and build an RC car to the best of the student’s ability. During the winter quarter the student was put to the test to manufacture the car that was designed in the previous quarter by using the 3D printers that prints in PLA plastic and using the CNC plasma cutter to cut out the aluminum sheet for the chassis plate. For this quarter the student will be doing all the testing to the car that were chosen back in fall quarter. The student has completed all the tasks that were given and will be able to compete in the ASME RC Baja competition. The student completed three tests: suspension deflection, impact, and top speed. The first test the suspension deflected 12.5% less than the predicted 2 inches during the 1.5-foot drop test. The top speed achieved was 20 MPH and the deflection on impact was 10% more than predicted.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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