Electroadhesive robotics experiment in simulated microgravity
Bibliographic record
Abstract
Canada has a strong presence in space robotics, and Mission SpaceWalker (MSW) is a group of ambitious young women who are leaning into Canadian robotics excellence through their investigation of how electroadhesive (EA) robots behave in reduced gravity, all while introducing a new testing procedure for rovers and adhesive space robotics. As an undergraduate student team at the University of Alberta, MSW has been chosen to fly their payload as part of the Canadian Reduced Gravity Experiment Challenge (CAN-RGX) organized by Students for the Exploration and Development of Space Canada (SEDS-Canada), the National Research Council of Canada, and the Canadian Space Agency. The annual competition is open nationwide to university students, soliciting participating groups to create a payload to be tested onboard a parabolic flight. The experiment will consist of an automated system contained within a Pelican case, featuring a mechanical design that will obtain footage of two robots on conductive and nonconductive surfaces. It will log data from onboard sensors to provide insight into the performance of the EA pads on both surfaces. The payload is expected to be flown on a parabolic flight during the CAN-RGX campaign in the 2023 calendar year.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".