A comparison of seismicity to the crustal deformation predicted by a glacial isostatic adjustment model in northern Canada and western Greenland
Bibliographic record
Abstract
The horizontal crustal strain-rates induced by glacial isostatic adjustment (GIA) in northern Canada and western Greenland region are compared to the spatial pattern of seismicity to determine whether earthquakes occur where GIA-predicted crustal strain-rates are high. For the comparison, an updated seismicity catalogue was created from the 2010 version of the NRCan Seismic Hazard Earthquake Epicentre File (SHEEF2010) catalogue and the Greenland Ice Sheet Monitoring Network (GLISN) catalogue of the Geological Survey of Denmark and Greenland (GEUS). Crustal motion rates were computed using the Innu/Laur16 ice-sheet history and the VM5a viscosity profile. This GIA model optimizes the fit to relative sea-level and vertical crustal motion measurements around Hudson Bay and the Canadian Arctic Archipelago (CAA). A region in Baffin Bay where predicted GIA strain-rates are high also features historically high seismicity, including the 1933 M 7.4 and the 1934 and 1945 M 6.5 earthquakes. Elsewhere, agreement is not strong, with zones of seismicity occurring where predicted horizontal crustal strain-rates are small and large crustal strain-rates occurring where earthquake occurrence is muted. Further investigations could include calculation of crustal stress-rates so that failure criteria may be assessed for Baffin Bay events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".