A Long-Range Transmission Network for Animal Sighting in the Wilderness
Bibliographic record
Abstract
When wild animals are monitored in the vast wilderness of Canada, data transmission is considerably challenging due to the lack of effective network service provided by telecom operators or carriers, especially in sparsely populated areas. A Long-Range Transmission Network for a wildlife detection system using low-power and low-cost embedded software and hardware is designed and implemented. The objective of the system is to transmit the results of wildlife identification with environmental data through independent long-range networking. The system consists of a Camera-embedded System for wildlife image capturing and environmental data logging, a user system for scanning images and notifications, and a LoRaWAN networking for Long-Range Transmission. Once a targeted animal is detected and identified, the system issues an alarm in the monitored area and sends a LoRa data frame to an application server for further analysis and user notification. The transmission distance of data is effectively extended through the relay between nodes. The system can process up to nine frames per second from the camera and identify the designated wildlife with high accuracy by asynchronous multi-threading in a low-cost embedded system. The application could be beneficial for a variety of purposes in the vast and diverse wilderness areas, such as traffic alarms for large wild animals’ crossing, monitoring wildlife migrations by biologists, or a warning system in urban areas when there is a potential threat to the public such as approaching dangerous animals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".