Bibliographic record
Abstract
RESUME Les drones, qu’on appelle aussi UAVs (Unmanned Aerial Vehicles) ou RPV (Remotely Piloted Vehicles), sont des avions sans pilote. Ils sont adoptes par des organisations militaires et civiles pour accomplir des tâches difficiles dans des environnements tres hostiles, sans aucun risque pour l’humain. Comme toute nation developpee, le Canada a recours a ces avions pour ses missions militaires de maintien de la paix et pour des missions civiles telles que la surveillance du littoral, la surveillance des grands territoires et pour l’aide lors d’operations de sauvetage. L’utilisation des UAVs est tres diverse, que ce soit pour des operations militaires ou civiles, et l’engouement pour ces engins est de plus en plus grand dans les pays industrialises. Les progres realises dans les technologies de controle, de detection et de calcul ont permis a ces vehicules de realiser des missions independantes du controle direct de l'operateur. L’itineraire peut etre planifie a l'avance de la mission et le drone peut alors l’executer automatiquement. La planification de cet itineraire efficace implique la determination de solutions permettant d’atteindre un certain but fixe, comme par exemple eviter que le drone soit detecte par les radars au cours de son itineraire, ou trouver l'itineraire le plus court en terme de temps de deplacement, ou encore la minimisation du cout de la mission.--------- ABSTRACT UAVs (Unmanned Aerial Vehicles) or RPV (Remotely Piloted Vehicles), are planes without pilots. They were adopted by military and civilian organizations to accomplish difficult tasks over hostile environments and without risk for humans. Like any other developed nation, Canada has used the UAVs for its military missions such as peacekeeping and for civilian missions such as coastal monitoring, surveillance of large areas and rescue operations. The applications of UAVs are very diverse, ranging from surveillance operations to combat operations through rescues. Advances in monitoring technologies, detection and computing have allowed these vehicles to perform missions without the direct control of the operator. The itinerary can be planned in advance and the UAVs can then run automatically. Planning efficient routes involves determining a specific solution with a precise objective like avoiding radar detection of the UAVs, finding the shortest path in terms of time, or minimizing the cost of the mission.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".