Compact Weather Sensor Node for Predicting Road Surface Temperature
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
We propose a compact road weather sensor node to predict the road surface temperature. This node is based on the model of the environment and temperature of roads (METRo) developed by the Government of Canada. The model requires an atmospheric forecast, the station configuration, and observation information as inputs. Observation data have commonly been produced by a road weather information system (RWIS), but this system is larger than necessary, and it has been difficult to maintain the surface sensors embedded on roads. We experimentally determined that the air and surface temperatures are key parameters for the model to predict the surface temperature. The proposed node was designed to be compact with an integrated environmental sensor for measuring atmospheric parameters and an infrared (IR) non-contact thermometer to obtain the surface temperature. In field tests with the prototype, we verified the good observation performance of the IR remote sensor by using a standard instrument for measuring the surface temperature. The results of the model based on data obtained by the prototype also showed excellent predictive performance during nighttime. This weather sensor node uses long range (LoRa) technology, which makes it suitable for long-range, low-power, and low-data-rate performance, suggesting the possibility of its commercialization.
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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 | 0.000 |
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