Act in Haste, Repent at Leisure: An Overview of Operational Incidents Involving UAVs
Bibliographic record
Abstract
Unmanned Airborne Vehicles (UAVs) provide significant operational benefits to many different military organisations. At present, however, most systems lack the reliability of conventional air support. This imposes considerable demands on the teams that must operate and maintain UAVs. It also creates considerable risks for the units that must retrieve these vehicles and for local populations during offensive and peace keeping operations. The lack of reliability further increases the workload on investigatory agencies, which must identify the causes of failure in increasingly complex airborne and ground-based systems. It is, therefore, important that we identify the lessons that can be learned from previous UAV mishaps. The following pages review the four most serious incidents involving Tactical UAVs (TUAVs) used by the Canadian Defence Forces during Operation ATHENA (August 2003-November 2005). The military demands of operations around Kabul created an urgent requirement for UAV support. However, the decision to rush the deployment of these systems contributed to technical and organisational risks that threatened safety and created the preconditions where mishaps were likely to occur.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".