Navigating the skies: examining the FAA’s remote identification rule for unmanned aircraft systems
Bibliographic record
Abstract
As technology and innovations in unmanned aerial vehicles progress, so does the need for regulations in place to create safe and controlled flying scenarios. The Federal Aviation Administration (FAA) is a governing body under the United States Department of Transportation that is responsible for a wide range of regulatory activities related to the United States airspace. In a recently published final rule, the FAA addresses several concerns such as the need for a system to identify all aircrafts flying in national airspace, as well as the implementation of a separate system from the prevalent Automatic Dependent Surveillance–Broadcast system to prevent interference with manned aircrafts. Their solution to these concerns is the deployment of remote identification (RID) on all unmanned aircraft systems (UAS) flying under its implied jurisdiction. While US governing agencies retain the use of the word UAS for now, the International Civil Aviation Organization terminology is remotely piloted aircraft systems. The FAA describes the RID implementation as a “ Digital license plate” for all UAS flying in the United States airspace. They outline additional policies including several options for compliance, operating rules, and design and production guidelines for manufacturers. As the September 2023 deadline for compliance draws near, this article highlights possible deployment applications and challenges.
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.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".