Northern Adriatic meteorological tsunamis: Assessment of their potential through ocean modeling experiments
Bibliographic record
Abstract
Abstract Potential for generation of meteotsunami waves via open ocean resonance has been documented for the shallow northern Adriatic, based on a set of barotropic numerical modeling experiments. Model simulations were forced by a bell‐shaped traveling atmospheric (air pressure, wind) disturbance, with shape and propagation parameters chosen in accordance with measurements done during several observed northern Adriatic meteotsunamis. Air pressure disturbances were found to generate much larger meteotsunami waves than wind disturbances, with wind disturbances having a limited influence in the very coastal and shallow areas only. Numerical simulations reveal that the most important factor for generation of large meteotsunami waves is matching between the speed of the atmospheric disturbance and the speed of long‐ocean waves. Already a small (∼10%) deviation from resonant conditions stops the wave growth and dramatically decreases height of predicted waves. A train of atmospheric disturbances can significantly increase maximum wave heights at selected locations at which multiple reflections and superimpositions of meteotsunami waves occur. Sensitivity of model simulations to resonant conditions and limited cross‐propagation width of atmospheric disturbance explain the localization of destructive meteotsunami waves in a limited area during destructive historic events. Mapping of maximum predicted wave heights indicates places with large meteotsunami hazard potential, matching the locations where real events were observed, and may be a useful tool for assessing vulnerability and risks in coastal areas during extreme sea level events.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".