Mountain Pine Beetle Monitoring with IoT
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
Outbreaks of forest pests cause large-scale damages, which lead to significant impact on the ecosystem as well as the forestry industry. Current methods of monitoring pest outbreaks involve field, aerial and remote sensing surveys. These methods only provide partial spatial coverage and can detect outbreaks only after they have substantially progressed across wide geographic areas. This paper presents an IoT system for real-time insect infestation detection using bioacoustic recognition via machine learning techniques. Specifically, we focus on detecting the Mountain Pine Beetle (MPB), which is the most destructive insect of mature pines in western North American forests. We present the design of the system and describe its various hardware and software components. Experimental results collected from a prototype implementation of the system are presented, which show that the system can detect MPB with 82% accuracy. We also demonstrate the applicability of our system in other noise monitoring applications, and report our experimental results on urban noise detection and classification.
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) | 0.001 | 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 it