Spatial and temporal coverage of a cargo-ship GNSS network to detect tsunamis
Bibliographic record
Abstract
<!--!introduction!--> Recent tsunamis demonstrate the urgent need for more densely spaced observations and direct measurements from the oceans. Most of the existing observing capacity is located on land or close to the shore, and often sparse (seismic network, land-based GNSS, tide-gauges and DART array), limiting our ability to predict, detect and respond to tsunamis. We propose a network of ships with GNSS systems as a way to fill this geodetic observation gap in the ocean by tracking changes in sea-surface height, and detecting even small, ~10 cm amplitude tsunamis of different origins. One year of navigation data from the commercial shipping fleet is used to generate statistical coverage maps of large ships for different epochs in the Pacific region which are overlapped with regions source of tsunamis and impacted by tsunamis. Some first results describe what a cargo-ship network might experience in terms of tsunami travel time and tsunami predicted amplitudes based on several tsunami models calculated over the Pacific. They clearly demonstrate that commercial shipping lines provide an excellent temporal and spatial coverage of the ocean globally. A focus on different regions indicates that the highest density of ships is near coastlines, and testing different tsunami origins helps understand more precisely how this network could improve regional early warning. By exploring the geographic relationship between tsunami sources, travel times and amplitudes with the ships locations, the discussion seeks to determine the ability of a defined ship network to provide effective warnings for the communities at risk and improve hazard mitigations.
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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 | 0.000 | 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".