Assessment of alternative manual control methods for small unmanned aerial vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is a summary of experiments to assess alternative methods to control a small (<25 kg; under Transport Canada rules, small UAV are classified as under 25 kg; Transport Canada. 2014. TP15263 – Knowledge requirements for pilots of unmanned air vehicle systems (UAV) 25 kg or less, operating within visual line of sight. Transport Canada. August. Available from http://www.tc.gc.ca/eng/civilaviation/publications/page-6557.html [accessed 17 August 2015]) unmanned aerial vehicle (UAV) in manual mode. While it is true that the majority of a typical UAV mission will be in automatic mode (i.e., using an autopilot) this may not always be the case during takeoffs and landings, or if there is a failure of the autopilot. The concept of a manual control backup mode during all flight phases remains in proposed UAV regulations currently being defined in Canada and the US. The research summarized in this paper is an attempt to assess the accuracy of several manual control options for a small UAV. The paper includes both a theoretical discussion of the task of manually controlling a small UAV airframe and results from a series of field experiments investigating the use of first-person view techniques.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it