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Record W2410662817 · doi:10.1109/icnsurv.2016.7486384

Addressing the drone data collection process for the Required Data quality

2016· article· en· W2410662817 on OpenAlexaboutno aff
Glyn Owen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsDroneComputer scienceData qualityData collectionProcess (computing)Quality (philosophy)Engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.353
GPT teacher head0.387
Teacher spread0.033 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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