Seismic hazard analysis: a comparative study
Bibliographic record
Abstract
The quantitative seismic hazard maps for the 1970s National Building Code of Canada were evaluated using the Davenport–Milne method. The Cornell–McGuire method is employed to develop recent seismic hazard maps of Canada. These methods incorporate the information on seismicity, magnitude-recurrence relations, and ground motion (or response) attenuation relations. The former preserves and depends completely on details of the historical seismicity; the latter smoothes the irregular spatial occurrence pattern of the historical seismicity into seismic source zones. Further, the Epicentral Cell method, which attempts to incorporate the preserving and smoothing aspect of these methods, has been developed. However, the impact of the adopted assumptions on the estimated quantitative seismic hazard has not been investigated. This study provides a comparative seismic hazard assessment using the above-mentioned methods and simulation-based algorithms. The analysis results show that overall the Davenport–Milne method gives quasi-circular seismic hazard contours near significant historical events, and the Cornell–McGuire method smoothes the transition of contours. The Epicentral Cell method provides estimates approximately within the former and the latter. Key words: epicentral cell method, probability, seismic hazard, Thiessen polygon, Voronoi, uniform hazard spectra.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".