Meteotsunami research in the Strait of Georgia: Critical observational contributions from a student school network on Vancouver Island
Bibliographic record
Abstract
Meteorological tsunamis are frequently destructive tsunami-like waves generated by small-scale atmospheric disturbances. Several devastating events occurred recently in various regions of the world oceans, including the Balearic Islands, Sicily, the Adriatic and Black seas, the Great Lakes, the west coast of South Korea, the Netherlands and the Persian Gulf. Although this phenomenon has been actively studied for more than 25 years, the exact mechanism (or mechanisms) responsible for producing these extreme events remains a puzzle. One of the major problems making it difficult to determine the physical process generating meteotsunamis is the absence of a network of simultaneously working precise tide gauges and microbarographs in the affected region. A unique set of high-resolution atmospheric data from the meteorological “school network” of 132 school stations became available for 2008-2019 for the area of southern Vancouver Island and nearby Gulf Islands located in the Strait of Georgia. These data, combined with 1-min sea level data from Canadian Hydrographic Service (CHS) and USA National Oceanic and Atmospheric Administration (NOAA) tide gauges, has enabled us to examine both the spatial and temporal features of mesoscale atmospheric disturbances and coincident properties of the associated sea level oscillations. The data analyses, supported by a series of numerical experiments, has made it possible to reconstruct observed events and to determine the specific atmospheric parameters producing the strongest sea level response in the southern part of the Strait of Georgia. These experiments have helped us to recognize the most effective (and hence, most hazardous) directions and speeds of propagating atmospheric disturbances and to identify “hot spots” along the coast that are under the highest risk of large meteotsunamis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".