Toward a Universal Sensor: Predictive Inference for Adaptive Sensing in Wildfires
Bibliographic record
Abstract
As climate-driven disasters such as wildfires become more frequent and unpredictable, there is an increasing need for sensing systems that can adapt to dynamic, uncertain environments. This paper presents the Universal Sensor, a cognitive-inspired, software-defined architecture grounded in predictive processing. The system operates as a continuous loop of prediction, sensing, comparison, and control, enabling real-time adaptation to changing conditions. It combines predictive modeling, adaptive multi-sensor data collection, and sensor fusion to interpret context and respond efficiently. Feedback-driven control and a modular policy memory support proactive resource management and lay the groundwork for future analogical transfer. We evaluate the system using a wildfire simulation based on data from the 2016 Fort McMurray event, demonstrating improved adaptability and resource use compared to static sensing strategies. In an 8-day simulation with 2,000 sensors, Universal Sensors reduced transmissions, energy use, and data volume by 36%, while preserving over 90% of critical hotspot events. These findings illustrate how predictive adaptation can enable efficient sensing at scale without sacrificing critical event coverage.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".