A Parametric Comparison of JARUS SORA 2.0 and 2.5 Ground Risk Models
Bibliographic record
Abstract
This paper provides a comparative analysis of the Joint Authorities for Rulemaking of Unmanned Systems (JARUS)’ Specific Operations Risk Assessment (SORA) ground risk model, between Version 2.0 and Version 2.5, focusing on differences and similarities. SORA, a methodology for risk assessment and conformity evaluation developed by JARUS, has been widely adopted across various regions, including Australia, Canada, the European Union, and others. The study delves into the variations in risk assessment outcomes concerning intrinsic and final Ground Risk Class, elucidating their implications for different categories of Unmanned Aircraft Systems (UASs). Key paradigm shifts between SORA 2.0 and 2.5 affecting Ground Risk assessment are outlined, as follows: (1) Introduction of quantitative analysis based on precise population density for determining intrinsic Ground Risk Class. (2) Incorporation of Visual Line of Sight (VLOS) from the remote pilot as a mitigation measure, coupled with a stricter definition of VLOS as visual ground control. (3) Enhanced differentiation among UAS sizes. Furthermore, the paper underscores the implications of these changes on original equipment manufacturers (OEM) and operators. By referencing standard industry operations, the analysis sheds light on how modifications in the SORA methodology impact UAS operations and regulatory compliance. Overall, this comparative analysis provides valuable insights into the evolution of the SORA ground risk model, facilitating a deeper understanding of its application in UAS operations and regulatory frameworks globally.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 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".