A next-generation open-source tool for earthquake loss estimation
Bibliographic record
Abstract
Earthquake loss estimation (ELE), generally also referred to as earthquake risk assessment, is a comparably young research discipline which, at first, relied on empirical observations based on a macroseismic intensity scale. Later, with the advent of methodologies and procedures that are based on theoretical simulation in estimating physical damage under earthquake loading, the analytical approach for ELE was formulated. The open-source software SELENA, which is a joint development of NORSAR (Norway) and the University of Alicante (Spain), is undergoing a constant development. One of the more recent features being included is the possibility to address topographic amplification of seismic ground motion. Additionally, SELENA has been adapted by including various methods for the analytical computation of structural damage and loss. SELENA now offers complete flexibility in the use of different types of fragility curves based on various ground motion intensity parameters (e.g. PGA, Sa, Sd), which has been suggested by many recently released guidelines (e.g. FEMA P-58, GEM-ASV, SYNER-G, HAZUS-MH). Besides, under the framework of the ongoing Horizon 2020 LIQUEFACT project, SELENA is extended in order to allow the consideration of liquefaction-induced ground displacements and respective structural damage.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".