Use of Bluetooth low energy and ultra-wideband sensor systems to detect people in forest operations danger zones
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
Forests are challenging workplace environments with rugged and steep terrains and large danger zones obscured by dense forest stands. Additionally, there are often restrictions on mobile communication networks, the Internet, or on Global Navigation Satellite Systems (GNSS) reception. Therefore, technologies supporting the detection of people in danger zones have not been broadly applied in forestry. During the field test, two prototypes enabling people detection via ultra-wideband (UWB) and Bluetooth low energy (BLE) were evaluated. The precision, accuracy, detection distance, and detection rates of the prototypes were determined. Furthermore, the influence of the line of sight, that is, the visual path between two points, was considered. With an overall Distance Bias of 0.44 m and overall RMSE of 1.52 m, the UWB sensor allowed precise detection within the danger zones, 30 m (mean detection distance, 28.4 m; 90% CI: 22.33–30.00 m) and 50 m (mean detection distance, 43.9 m; 90% CI: 36.81–49.63 m); therefore, it is well suited for use during felling with a chainsaw. The BLE sensor allowed presence detection even at greater distances (mean detection distance, 83.66 m; 90% CI: 62.45–103.05 m) and would be suitable for fully mechanized timber harvesting. However, BLE sensors still lack the ability to determine detection distances.
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 | 0.001 | 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