A review of human factors research performed from 2014 to 2017 in support of the Royal Canadian Air Force remotely piloted aircraft system project
Bibliographic record
Abstract
Remotely piloted aircraft systems (RPASs) are tools for military organizations to help remove humans from dangerous situations and permit operations in severe and inhospitable environments. To support the procurement of an RPAS fleet under Canada’s Strong, Secure, Engaged 2017 defence policy, the Royal Canadian Air Force (RCAF) under the RCAF Joint Unmanned Surveillance and Target Acquisition System project (subsequently replaced by the RCAF RPAS project) funded Defence Research and Development Canada – Toronto Research Centre to conduct a preliminary investigation of human factors (HF) issues relating to the performance of the crew in the ground control station (GCS) to control a RPAS. This paper presents a review of the RCAF research program conducted between 2014 and 2017 that discusses HF issues in RPAS operations and how training is associated with the HF attributes of decision-making, skills and knowledge, and mission preparation. Also, this paper presents a training needs analysis methodology and analysis that identified essential RPAS crew competencies (expressed as the knowledge, skills, and abilities required by each crew member to perform their respective tasks). Finally, this paper discusses work that investigated experimentation and evaluation capabilities to support RPAS operator training and GCS airworthiness certification.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".