The 2018 Sulawesi tsunami in Palu city as a result of several landslides and coseismic tsunamis
Bibliographic record
Abstract
The September 28 2018 Palu tsunami surprised the scientific community, as neither the earthquake magnitude nor its strike-slip mechanism were deemed capable of producing the wave heights that were observed. However, recent research has shown that the earthquake generated several landslides inside Palu bay. The authors conducted a post-disaster field survey of the area affected to collect spatial data on tsunami inundation heights, nearshore and bay bathymetry, and carried out eyewitness interviews to collect testimonies of the event. In addition, numerical simulations of the tsunami generation and propagation mechanisms were carried out and validated with the inferred time series. Seven small submarine landslides were identified along the western shore of the bay, and one large one was reported on the eastern shore of Palu City. Most of these landslides occurred at river mouths and reclamation areas, where soft submarine sediments had accumulated. The numerical simulations support a scenario in which the tsunami waves that arrived at Palu city 4–10 min after the earthquake were caused by the co-seismic seafloor deformation, possibly coupled with secondary waves generated from several submarine landslides. These findings suggest that more comprehensive methodologies and tools need to be used when assessing probabilistic tsunami hazards in narrow bays.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".