Bibliographic record
Abstract
A “patchwork” of domestic drone regulations that differ from country to country are being created, confusing operators and manufacturers in an industry that is predicted to evolve into a US$93 billion market in 10 years. Another report states that “the market for UAVs for Defense and Security operations will undergo periodic and regional fluctuations resulting from significant economic and technological changes. Global procurement over that period will exceed $130 Billion.” Not mentioned in these regulations is the quality and management of data required. For example, an initial step in drone data collection is the process of drone registries. Drone registries already exist within the United States, Canada, Ireland, United Arab Emirates, Switzerland, Australia, Hong Kong, Singapore and other countries - and are already dissimilar in such fields as size, weight, operational usage, etc. The International Civil Aviation Organization's (ICAO) Annex 15, Chapter 1, implies the necessity for a Quality Management System (QMS) when it states “Corrupt or erroneous aeronautical information/data can potentially affect the safety of air navigation.” Examples would include Notices to Airmen (NOTAMs), Pre-flight Information Bulletins (PIBs) and Aeronautical Information Circulars (AICs). Similar to these products and services, but possibly unobserved, is the inherent and essential need to fulfill specific requirements to meet the needs of drone users, no matter their intended legitimate purpose. In other words, with the explosive growth of drone usage, the need for aeronautical information / data of a required quality (e.g., adequacy, availability, timeliness, etc.) has never been greater than in the coming air navigation environment in which a higher accuracy of data will be required. Within the European Community (EC), a regulated subset of QMS is Aeronautical Data Quality (ADQ). The EC implementing rule for ADQ is EC Regulation 73/2010. The process of collecting drone data and meeting their aeronautical data quality standards was successfully implemented by the Irish Aviation Authority (IAA). How the Irish Aviation Authority (IAA) met their ADQ compliancy when they established their drone registry is the subject of this paper.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".