Performance of Satellite Fog Detection Techniques With Major, Fog- related Highway Accidents
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
Five multi- vehicular highway accidents caused by low visibilities in fog were examined as to the ability of Geostationary Operational Environmental Satellite (GOES) techniques to detect the fog in advance. All of the accidents occurred near or shortly after sunrise on major U. S. or Canadian highways and resulted in numerous injuries and some fatalities. Multi- spectral infrared and visible channel data were used in the evaluation. In most cases, fog was detectable from GOES products but the lead time was usually short (1- 3 hours). All were mesoscale events that would have required use of all available forms of observational data from satellites and surface mesonets to properly diagnose. Benefits and shortcomings of satellite- based techniques are described, along with technology improvements planned for future spacecraft. 1.
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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) | 0.000 | 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