GeoSurv II Unmanned Aerial Vehicle: A major undergraduate project with
Bibliographic record
Abstract
A final year undergraduate capstone design project was run in 2010-2011 and involved 32 students, 8 lead engineers and a project manager. The project was to design, develop and build an unmanned aerial vehicle (UAV) that can be used for geophysical surveying and was carried out in the Department of Mechanical and Aerospace Engineering at Carleton University. It built upon previous years work and involved students mostly from the Aerospace Engineering and Mechanical Engineering BEng programs. However, there was cross-department input, with students from the BEng programs Computer and Systems Engineering, Software Engineering and Communication Engineering, who undertook their final year project working on the avionics. Students were split into groups with lead engineers guiding the groups and individuals, plus there was an overall project manager. The groups were aerodynamics, avionics, flight test, integration and structures. The lead engineers were faculty, sessional lecturers and one graduate student teaching assistant. Sessional lecturers brought experience from the military and government research laboratories. Individual groups met weekly and additionally all students from all groups met collectively, again weekly, to share and discuss progress and issues. Students were required to produce design reports, as well as a group final report and each term a formal design review. Besides the development of GeoSurv II a new UAV design, named Corvus, was started and 10 students, drawn from across the different groups, worked on the preliminary design with the aim to produce a demonstrator vehicle. This paper describes the organization and running of such a large, multidisciplined group project as well as giving details of student opinions taken throughout the project's progress over the academic year.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".