The Liability Regime for the Transportation of Goods by Drones: Is There a Need for a European Union Initiative?
Bibliographic record
Abstract
The use of civil Unmanned Aircraft System (UAS) is becoming more common place, and the application and development of this technology open the door for numerous opportunities, particularly from a commercial perspective. This article first provides an overview of the liability regime applicable in the European Union for the transportation of goods by UAS (otherwise known as drones), or more precisely, emphasises the lack of a liability regime adapted to unmanned aircraft operations within the Single European Sky. Second, an overview of the liability regime for the transportation of goods under the Montreal Convention of 1999 will be studied for its potential applicability to unmanned aircraft operations which will further underscore the lack of any such adaptability of that regime to unmanned aircraft operations. Finally, this article raises the question of whether the creation of a liability regime specially designed for the use of unmanned aircraft in the transportation of goods is needed – one which would take into account all of the specificities related to this advancing technology that cannot be adequately encompassed within the existing regimes governing manned aviation. Unmanned Aircraft System, Cargo, Liability, European Union
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".