Probabilistic tsunami hazard assessment for the makran subduction zone using logic tree and stochastic rupture sources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Makran Subduction Zone (MSZ) in the northwestern Indian Ocean can generate large tsunamigenic thrust earthquakes affecting the coastal regions of Pakistan, Iran, Oman, and western India. In this paper, a probabilistic tsunami hazard assessment is conducted for the MSZ using stochastic tsunami simulations of moment magnitude (Mw) of 7.7–9.1 earthquake scenarios. This study investigates uncertainties associated with earthquake occurrence rate, single-segment (eastern and western MSZ) or two-segment (full MSZ) rupture scenarios, source geometry, and slip heterogeneity. The total number of simulated source models is 15,000. This study presents two major categories of results: stochastic source models and ranges of 475, 975, and 2475-year tsunami heights. For instance, tsunami heights generated by Mw 8.5‒8.7 stochastic sources of western MSZ vary between 1 m and 10 m with a mean of ~ 4.5 m in the affected areas. The tsunami heights are sensitive to the source models’ characteristics, such as location of the large slip areas, bathymetry of the nearshore area, and the location of bays. Considering different occurrence rates results in significant variability in the estimated 475, 975, and 2475-year tsunami heights. For example, 2475-year tsunami height in Chabahar is in the range of 3‒7.4 m at 10 m water depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 it