The Lifecycle of Marine Fog in the Coastal Zone
Bibliographic record
Abstract
Fog is a collection of suspended water droplets or ice crystals in the atmospheric boundary layer that appears under favourable dynamic, thermodynamic, chemical, and surface conditions with visibility less than 1 km. Fog negatively affects society due to its ability to reduce visibility, which leads to economic losses of the same enormity of tornadoes. Notwithstanding extensive research, the ability to predict fog accurately is limited. Coastal fog is particularly difficult to predict because of the complex interactions between the lower atmosphere, upper ocean, and land surface. This dissertation presents several cases studies that aim to improve the understanding of coastal fog. The first part of the dissertation investigates a captivating fog dissipation event that was observed during the ’Fog and Turbulence Interactions in the Marine Atmosphere’ (FATIMA) project’s Grand Banks field campaign in the North Atlantic, where a fog-free region appeared immediately downstream of an islet (Sable Island) as fog advected past it. The second part concerns a mixing-fog event that appeared during the C-FOG field campaign off the coast of Newfoundland, Canada, where an oceanic cold front collided with a peninsula to produce misty/foggy conditions. In the third part of the dissertation, to aid fog prediction using numerical weather prediction models, a comprehensive set of laboratory experiments was conducted to quantify mixing as a function of the obstacle height.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".