Bibliographic record
Abstract
In the last 10 years, Military Airworthiness Authority (MAA) awareness of their responsibilities when acquiring aircraft, services or information from other airworthiness authorities has increased. One specific incident triggered this; a North Atlantic Treaty Organisation (NATO) chartered aircraft accident that resulted in the death of 13 crew and 62 NATO troops returning from Afghanistan, for which NATO was found partially accountable and had to pay compensation for failing in their duty of care. Now, MAAs world-wide are collaborating in mutual recognition activities and applying greater diligence in their assessments for seeking and obtaining services from other MAAs. In response to this global collaboration, Australia's Military Airworthiness Technical Regulator established a Mutual Recognition team to examine the relationships between MAAs, and to participate in the Air and Space Interoperability Council (ASIC) Airworthiness Project Group. Through the Project Group, Australian Defence in partnership with the United States (US) Army, US Navy, US Air Force, United Kingdom, Canada and New Zealand have formalised a recognition process for other airworthiness systems. The Australian Defence Force (ADF) relies heavily on other airworthiness authorities for the acquisition and sustainment of their aviation assets. Current interactions are acknowledged through a series of different mechanisms, both formal and informal. This paper will provide a snapshot of the current state of airworthiness interactions and a glimpse of the potential future state provided through formalised recognition.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".