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.In general, software tools for ELE are particularly useful in two different settings, i.e., for disaster management and (re)insurance purposes.Both sectors pose very different demands on ELE studies: while the (re)insurance sector is foremost interested in the direct and indirect economic losses caused by an earthquake to its insured physical assets, those institutions (often governmental and nongovernmental organizations) in charge of disaster emergency management and response are more interested in reliable estimates on human losses and the potential short-and long-term social consequences.Being aware about these peculiar differences between software tools for disaster management and insurance applications, NORSAR/UA thereby offers two in its core similar software tools, i.e., the opensource software SELENA and the proprietary software PML (Probable Maximum Loss) which is actively used by the insurance association in Chile (South America) since 2011.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.028 |
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".