A procedure for delineating a search region in the UAV-based SAR activities
Bibliographic record
Abstract
We propose a simple geometrical approach for delineating a region above which an Unmanned Aerial Vehicle (UAV) should fly to support the Search and Rescue (SAR) activities. The procedure is based on the concept of a crow's flight distance travelled by a lost person and its probability distribution, for areas in which there does not exist any SAR database that can be used to estimate parameters of such a distribution. The novelty of the procedure lies in its indirect character, namely we do not estimate these parameters but we seek regions that reveal comparable topographic settings in order to borrow the parameters from where they are known. Our analysis focuses on the Wakeby probability distribution of the crow's flight distance, the parameters of which are known for Alberta in Canada. We compare topographic and ecological characteristics of Alberta with the same features in Poland and argue that – under a few assumptions – it is allowed to use the Wakeby probabilistic model for the Canadian region in Polish conditions. Having borrowed the parameters in question, we present the skills of the geometrical approach in an experiment that utilizes flight simulations carried out with two professional micro UAV systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".