Seismic site amplification assessment in the Dakwäkäda (Haines Junction) area of Yukon, Canada, from probabilistic inference of passive seismic measurements
Bibliographic record
Abstract
The Dakwäkäda (Haines Junction) area is in a tectonically active region of Yukon, Canada, with significant natural hazard potential. Despite this, little knowledge exists about local site properties, which influence earthquake shaking intensity and duration (site effects). This work constrains sediment properties to quantify these effects. Passive seismic recordings at 14 sites are used to extract dispersion measurements and probabilistically infer 1D subsurface shear-wave velocity ( V S ), including rigorous model uncertainty quantification. The V S models are used to classify sites according to proxies for site rigidity. Results, including uncertainties, are propagated into estimates of linear site amplification factors for earthquake ground motion determination. Our results indicate much of the area can be characterized by site class C and modest amplification potential. Spatial variability in our results is predominantly attributed to hydrologic and cryospheric processes. The presence of permafrost in the area may currently mitigate amplification of earthquake shaking. Our results point to the need to understand seasonal and long-term changes in site effects, particularly in response to permafrost thaw within the warming climate. Results from this work can contribute to strategic community planning that mitigates natural hazards in the Dakwäkäda area, and other seismically active areas throughout the global North.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".