Use of Bluetooth low energy and ultra-wideband sensor systems to detect people in forest operations danger zones
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.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".