Lakes as a Source of Short‐Period (0.5–2 s) Microseisms
Bibliographic record
Abstract
Abstract We identify and document microseisms produced by wave action in six lakes: The Great Slave Lake, Lake Ontario, Yellowstone Lake, Dianchi Lake, Fuxian Lake, and Erhai Lake. The lakes span more than 2 orders of magnitude in size (areas of 210–27,000 km2) and sample a range of climatic and tectonic regimes in Canada, the U.S., and China. Lake‐generated microseisms create spectral peaks at periods near 1 s and are often polarized as Rayleigh waves propagating away from the lake. In contrast to ocean‐generated microseisms, lake‐generated microseisms are only observed within about 25–30 km of the shoreline. This is consistent with the well‐known high attenuation of short‐period Rayleigh waves (Rg). It is unclear if lake‐generated microseisms are produced by a linear shoaling process, analogous to primary ocean microseisms, or a nonlinear wave‐wave interaction process, analogous to secondary ocean microseisms. If they are mainly produced by shoaling, lake‐generated microseisms might provide a spatially integrated measure of shoreline erosion. Regardless of the source mechanism, lake‐generated microseisms appear to provide a record of ice phenology for lakes that freeze in the winter. Such data could contribute to assessing the effects of climate change on high‐latitude lakes in remote areas. Finally, it is likely that lake‐generated microseisms are useful for imaging the geological structure of the shallow crust, information that is important for quantifying seismic hazard and can be difficult to obtain in urban areas where active source imaging is not feasible.
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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 | 0.000 |
| 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) | 0.001 | 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".