SORA tool—a specific operation risk assessment tool for civilian drone operations
Bibliographic record
Abstract
The rapid expansion of unmanned aerial system (UAS) technologies, coupled with the increasing complexity of regulatory frameworks, highlights the need for innovative solutions that streamline the UAS operation approval process. This paper presents the development and implementation of a web-based tool designed to simplify the approval process in accordance with the specific operations risk assessment (SORA). While the tool was primarily developed for member state of the European Union (EU), SORA is developed by Joint Authorities for Rulemaking on Unmanned Systems (JARUS) and is therefore also used in countries outside the EU. Employing the Double Diamond methodology, the SORA process was divided into three distinct phases: Evaluation, Demonstration, and Submission. The resulting tool guides users through each phase, offering step-by-step guidance, automating calculations, and generating all the documentation required for submission. While the SORA tool has potential to improve efficiency within the SORA process, it has limitations for higher risk operations. Future enhancements could focus on improving integration with national aviation authorities and expanding its collaborative capabilities. This study contributes to ongoing efforts to digitise and streamline regulatory processes in the rapidly evolving field of drone operations, ultimately fostering a more diverse and vibrant UAS ecosystem.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".