Indication of glacially-induced fault reactivation in Latvia, Lithuania and the Kaliningrad District of Russia from models of glacial isostatic adjustment
Bibliographic record
Abstract
We model the change of Coulomb Failure Stress (δCFS) during the Weichselian glaciation up until today at 12 locations in Latvia, Lithuania and Russia that are characterised by soft-sediment deformation structures (SSDS). If interpreted as seismites, these SSDS may point to glacially-induced fault reactivation. The δCFS suggests a high potential of such reactivation when it reaches the instability zone. We show that δCFS at all 12 locations reached this zone several times in the last 120,000 years. Most notably, all locations exhibit the possibility of reactivation after ca. 15 ka BP until today. Another time of possible activity likely happened after the Saalian glaciation until ca. 96 ka BP. In addition, some models suggest unstable states after 96 ka BP until ca. 28 ka BP at selected locations but with much lower positive δCFS values than during the other two periods. For the Valmiera and Rakuti seismites in Latvia, we can suggest a glacially-induced origin, whereas we cannot exactly match the timing at Rakuti. Given the (preliminary) dating of SSDS at some locations, at Dyburiai and Ryadino our modelling supports the interpretation of glacially-induced fault reactivation, while at Slinkis, Kumečiai and Liciškėnai they likely exclude such a source. Overall, the mutual benefit of geological and modelling investigations is demonstrated. This helps in identifying glacially-induced fault reactivation at the south-eastern edge of the Weichselian glaciation and in improving models of glacial isostatic adjustment. This work has been published in Steffen et al. (2019). Reference: Steffen, H., Steffen R., Tarasov L. 2019. Modelling of glacially-induced stress changes in Latvia, Lithuania and the Kaliningrad District of Russia. Baltica, 32 (1), 78–90.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".