Bibliographic record
Abstract
Material testing is a crucial part of engineering design and development, especially in aerospace engineering as it is more cost effective to test an element or component than doing a full-scale test on the completed part. Typically, uniaxial testing is carried out to characterize a material, which is adequate for finding the properties of the material. However, these kinds of tests are inadequate for simulating the real-world loading that a component may experience in its life cycle. Therefore, this project’s goal was to develop a low-cost biaxial testing apparatus using off-the-shelf components, including the Arduino Uno microcontroller (“Arduino”), stepper motors (“motors”), and load cell. This report outlines the development of the software required to operate the motors and read output value of the load cell. The Arduino code used to control the motors was developed using open-source code available on GitHub and the Stepper library, which contains the required functions for controlling the motors. The Arduino code can be used to determine the strain rate of up to 11 𝑚𝑚/𝑚𝑖𝑛, as well as the type of loading (tension or compression) along each axis.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".