Relations between source parameters for large Persian earthquakes
Bibliographic record
Abstract
Empirical relationships for magnitude scales and fault parameters were produced using 436 Iranian intraplate earthquakes of recently regional databases since the continental events represent a large portion of total seismicity of Iran. The relations between different source parameters of the earthquakes were derived using input information which has usefully been provided from the databases after 1900. Suggested equations for magnitude scales relate the body-wave, surface-wave as well as local magnitude scales to scalar moment of the earthquakes. Also, dependence of source parameters as surface and subsurface rupture length and maximum surface displacement on the moment magnitude for some well documented earthquakes was investigated. For meeting this aim, ordinary linear regression procedures were employed for all relations. Our evaluations reveal a fair agreement between obtained relations and equations described in other worldwide and regional works in literature. The M0-mb and M0-MS equations are correlated well to the worldwide relations. Also, both M0-MS and M0-ML relations have a good agreement with regional studies in Taiwan. The equations derived from this study mainly confirm the results of the global investigations about rupture length of historical and instrumental events. However, some relations like MW-MN and MN-ML which are remarkably unlike to available regional works (e.g., American and Canadian) were also found.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 0.001 |
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".