Night Icing Potential Demonstration Project
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
This paper presents a very cost-effective approach for the preparation of thermal fingerprints and the forecasting of potential night icing situations. A Nova Scotia Transportation and Public Works (NS TPW), Canada, patrol vehicle equipped with an infrared (IR) sensor and an automatic vehicle location (AVL) service was used to perform IR data runs along a section of Highway 104 in Pictou County, Nova Scotia. The signal from the IR sensor was fed directly into the AVL unit, which relayed the positional, timing, and temperature information directly to the AVL provider, Grey Island. AMEC meteorologists coordinated the IR runs with NS TPW staff and extracted the Grey Island AVL data daily for analysis against the weather from the previous night. The data were mathematically filtered, aligned, and averaged. Thermal fingerprints for three weather types (extreme, intermediate, and damped) were produced in a geographic information system (GIS) format. The thermal fingerprints for Highway 104 were then associated with the two roadway weather information systems along the route. The route was divided into equal segments, and the coldest temperature deviation from the mean along each segment was assigned to the entire segment. Forecasts of pavement temperature and air dew point were used with the fingerprint corresponding to the coming nights prevailing forecast weather to determine the earliest time at which frost could form for each road segment. The resulting GIS map with color-coded road segments and time stamps of the potential onset of icing provides an effective new road maintenance operations planning tool. A GIS-based format for thermal fingerprints and forecast presentation will be presented. The logic and steps in the production of this innovative night icing potential chart product will be presented and its limitations described.
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 | 0.001 | 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