A procedure for delineating a search region in the UAV-based SAR activities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it