Large-Scale Synoptic Systems and Fog During the C-FOG Field Experiment
Bibliographic record
Abstract
Abstract The goal of this work is to summarize synoptic meteorological conditions during the Coastal Fog (C-FOG) field project that took place onshore and offshore of the Avalon Peninsula, Newfoundland, from 25 August until 8 October 2018. Visibility was measured at three locations at the Ferryland supersite that are about 1 km from each other, and at two additional sites 66 and 76 km to the north. Supporting meteorological measurements included surface winds, air temperature, humidity, pressure, radiation, cloud-base height, and atmospheric thermodynamic profiles from radiosonde soundings. Statistics are presented for surface measurements during fog events including turbulence kinetic energy, net longwave radiation, visibility, and precipitation. Eleven fog events are observed at Ferryland. Each significant fog event is related to a large-scale cyclonic system. The longest fog event is due to interaction of a northern deep low and a tropical cyclone. Fog occurrence is also examined across Atlantic Canada by including Sable Island, Yarmouth, Halifax, and Sydney. It is concluded that at Ferryland, all significant fog events occur under a cyclonic system while at Sable Island all significant fog events occur under both cyclonic and anticyclonic systems. The fog-formation mechanism involves cloud lowering and stratus broadening or only stratus broadening for the cyclonic systems while for the anticyclonic systems it is stratus broadening or radiation. Although widely cited as the main cause of fog in Atlantic Canada, advection fog is not found to be the primary or sole fog type in the events examined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".