Large-Scale Environmental Sensing of Remote Areas on a Budget
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
By enabling large-scale in-situ environmental monitoring of remote areas, the Internet-of-Things (IoT) can play a crucial role in quantifying and responding to climate change. Sensing of uninhabited and many rural regions creates a need for inexpensive battery-powered IoT systems that can be deployed across large areas. Today, such systems are woefully unavailable. This article presents a scalable IoT architecture for low-cost and low-power in-situ environmental sensing. The architecture is anchored by self-organizing LoRa mesh networks that can be scaled to a hundred nodes, covering a hundred or more square kilometers, at a cost of less than US$15 per node. A low-power design enables nodes to operate for years on two AA batteries in many sensing applications. LoRa mesh networks connect to a cloud-based IoT backend via a battery-powered modular gateway, which supports Internet access over a WiFi network, a cellular network, and a low-earth orbit satellite system.
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) | 0.000 | 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 it