Moment magnitude (MW) conversion relations for use in hazard assessment in offshore eastern Canada
Bibliographic record
Abstract
Seismic hazard assessments based heavily on earthquake recurrence rates require that the same magnitude scale be used for all earthquakes evaluated to ensure that the assessment is unbiased and uniform across the area of interest no matter how large. Moment magnitude, MW, is generally seen as the magnitude of preference in current practice. However, it was not routinely calculated in the past for earthquakes in Canada, necessitating the conversion from other magnitude types in common use. This paper focuses on the offshore regions of eastern Canada, including the eastern Arctic, where ML is the day-to-day magnitude scale. Conversions to MW are established and evaluated. Until very recently there were few MW values determined for offshore earthquakes. In recent years, however, regional centroid moment tensor inversions have been run on a routine basis for earthquakes in this region allowing us to build up a database of moment magnitudes for the offshore. While the dataset is still smaller than for the adjacent onshore regions and somewhat restricted in magnitude range, it has enabled the development of an ML-MW conversion relation for offshore eastern Canada, which shows that, on average, ML is 0.21 magnitude units greater than MW. Statistical tests show no advantage to using a linear relation over a straight constant conversion.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".