Data acquisition software for port interfacing devices
Bibliographic record
Abstract
Data acquisition is a problem that is frequently encountered in the world of research and experimentation. There are many existing software applications that are abstract enough to handle data acquisition for a large range of scenarios, but sometimes a more specialized approach is needed. I designed the Data Logger application to be a lightweight standalone application to translate data sent from multiple serial-ports, which interface with IMUs (Inertial Measurement Units), and then record the data in log files. These log files would then be used during post experimental processing to determine conclusions based on IMU performance. This application was written in Java using only one external library, the Java Communications API (also known as javax.comm). The software was designed with extensibility in mind. This is to say that it would be easy for any other serial-port interfacing device (not just IMUs) to have its data recorded by Data Logger. All that’s required is for someone to implement the translation of the data streaming from the serial port. The software architecture is also setup so that it should be simple to add new modules to communicate with and record data from other sources, including different kinds of ports. Throughout design and implementation, a few concerns and problems were noticed and addressed. Solutions have been found for some, such as accurate time stamping, but there are other issues that remain unsolved, such as time-synchronization.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.108 | 0.055 |
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".