Seasonal wireless sensor network link performance in boreal forest phenology monitoring
Bibliographic record
Abstract
While indoor wireless sensor network (WSN) research has recently flourished for monitoring civil and industrial infrastructure, considerably less attention has been given to the development of reliable outdoor WSNs capable of long-term operation in challenging remote locations. We present wireless sensor network link performance results from the first year of monitoring micro-meteorological conditions alongside the 802.15.4 link received signal strength indicator (RSSI) within an old growth stand of deciduous boreal Aspen forest (Populus tremuloides) in Northern Alberta, Canada. Thirty-six weather proof nodes were equipped with meteorological sensors and distributed across one hectare in the forest understory to assess the application of WSNs for observing high resolution changes in seasonal ecosystem productivity and forest phenology. We describe here the density distribution of node RSSI using Gaussian kernel density estimates in relation to node antenna-receiver orientation and vegetation seasonality. RSSI across the network displays a lognormal distribution with an increasing bimodal tendency with path length through the forest stand. Spatial variability in RSSI is discussed with respect to forest structure. A strong temporal relationship between RSSI variability and plant canopy development is observed with a 20dBm or 100 fold difference in mean network radio signal power from spring leaf presence to fall leaf absence. The meteorological and biophysical factors associated with this trend are explored using multiple regression and relative factor importance analysis. Our results indicate that in addition to meteorological data, spectral vegetation density metrics are useful in assisting deployment planning and network performance diagnostics when using wireless sensor networks for remote forestry applications. The longevity and performance of this outdoor WSN can be seen as a new standard for harsh network-climate tolerance in northern boreal environments.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".