Fall 2017 Seminar Series - Alongshore Variability in Nearshore Conditions on Barrier Island Beaches
Bibliographic record
Abstract
Presenter Bio Ryan Mulligan is a coastal engineer and oceanographer, with interests in the physical forces that cause changes to coastal regions and the ways in which coastal systems respond. Coastal processes act over a range in time scales from seconds (like surface waves) to hundreds of years (like sea level rise), but often it is timescales of days (like hurricanes and storm events) over which major changes such as erosion occurs that affect human populations. Coastal processes can also act over a wide range of spatial scales from sub-millimetre scale (like fluid turbulence) to thousands of kilometres (like tsunamis) and it is important to understand the interaction of many different processes to simulate and predict future changes to the coastal environment. Dr. Mulligan's interests range from surface waves, ocean currents, transport of water and sediments and contaminants to changes in the geomorphology of the coastline and seabed. He uses field observations and numerical models to study coastal systems, and develop further understanding of the processes that affect oceans, estuaries and rivers. His particular interest is in coastal regions that are exposed to severe storms including hurricanes, with large waves and strong currents, and understanding coastal erosion and flooding. He is also interested in marine renewable energy and its impacts on the marine environment, specifically as it relates to marine tidal current turbines (e.g., in the Bay of Fundy) and offshore wind turbines (e.g., in Lake Ontario).
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.006 |
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".