The Fog Remote Sensing and Modeling Field 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
A field project that includes surface observations, remote sensing, and forecast models provides a better understanding of fog-induced low visibility and improves the parameterization of fog microphysics. J l T he total economic loss associated with the impact of fog on aviation, marine, and land transportation can be comparable to those of winter storms. For example, in the pre-Christmas period of 20-23 December 2006, the British Airport Authority (BAA) reported that a blanket of fog and freezing fog over the United Kingdom (UK) forced 175,000 passengers to miss flights from its seven British airports, with Heathrow being the worst affected Early estimates suggested that this disruption to air travel cost British Airways at least 25 million The costs to stranded passengers in terms of money and inconvenience may be impossible to calculate. Previous studies have also shown that human and financial losses due to accidents related to fog episodes are very common. In Canada, approximately 50 people per year die because of motor vehicle accidents (Gultepe et al. 2007a) in which fog was a contributing factor (Transport Canada Report 2001). In describing ground transportation in Illinois, Westcott (2007) stated that approximately 4,000 accidents and 30 deaths occur annually under foggy conditions in Illinois, excluding the city of Chicago. In Europe, a major fog project called Cooperation in Field of Scientific and Technical Research (COST-722), with objectives of reducing economic loss and fatalities, was also created to develop advanced methods for very short-range forecasts of fog and low clouds m A dense fog event with low visibility values of about 50 m occurred on 27 Dec 2008. On this day, there was at snow on the ground in Toronto, Ontario, Canada when rain started at about 9:00 a.m. local time. The combina falling and snow on the ground with temperatures reaching up to I0C resulted in very dense fog in the boi
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 | 0.001 | 0.001 |
| 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