Work in progress - using internet applications to control remote devices for an instrumentation laboratory
Bibliographic record
Abstract
Undergraduate engineering students have undertaken a research project on the creation and the development of an Internet based real-time access to laboratory devices. SelfLab@Home is a novel tele-education project of the Department of Electrical and Computer Engineering of the University of Calgary. Its original objective was to become a self-paced remotely accessed training for the use of four basic laboratory devices: oscilloscope, waveform generator, DMM (digital multimeter) and a power supply. The high-level design components include a client interface, a client/server interface, a main server, a server/hardware interface, the agilent oscilloscope, and a video streaming scheme. The implementation of this project required the following components: client Web browser interface, Web server, application server, hardware dynamic link library (DLL), and video streaming scheme. A joint team of high school students enrolled in the research enrichment program and fourth year students have built this remotely accessed instrumentation laboratory to give all undergraduate students a chance to learn how to operate the equipment from outside the lab while working at their own pace.
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 | 0.011 | 0.009 |
| 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 | 0.005 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".