Bibliographic record
Abstract
Using 155 distributed seismic stations spanning Alaska and western Canada, we document how environmental factors like storms and sea ice influence microseismic noise. We examine power spectral densities of continuous seismic data and focus on secondary microseisms (5-10s) and short period secondary microseisms (1-2s). We cross-correlate the height of ocean waves across the region with the power spectral density time series. We find that the Gulf of Alaska is the dominant source of secondary microseisms in Alaska. The eastern Gulf, in particular, produces more energetic secondary microseisms despite, at times, lower overall wave amplitudes. We find that the short period secondary microseismic noise is produced in the coastal waters and attenuates quickly moving inland. We show that this band is heavily modulated by the influence of sea ice in the coastal ocean by comparing it with sea ice concentrations. We also document how these two microseismic bands vary seasonally and spatially as they respond to different environmental phenomena. We find that this seismic energy tracks closely the seasonal arrival and departure of sea ice in the coastal waters. As an example of how microseism energy can be used to track environmental change, we demonstrate an application of the short period microseism using it as a proxy for the annual season of coastal erosion.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".