Bear Encounters with Seismic Stations in Alaska and Northwestern Canada
Bibliographic record
Abstract
ABSTRACT A typical seismic experiment involves installing 10–50 seismometers for 2–3 yr to record distant and local earthquakes, along with Earth’s ambient noise wavefield. The choice of the region is governed by scientific questions that may be addressed with newly recorded seismic data. In most experiments, not all stations record data for the full expected duration. Data loss may arise from defective equipment, improperly installed equipment, vandalism or theft, inadequate power sources, environmental disruptions (e.g., snow covering solar panels and causing power outage), and many other reasons. In remote regions of Alaska and northwestern Canada, bears are a particular threat to seismic stations. Here, we document three recent projects (Southern Alaska Lithosphere and Mantle Observation Network, Fault Locations and Alaska Tectonics from Seismicity, and Mackenzie Mountains EarthScope Project) in which bears were regular visitors to remote seismic stations. For these projects, there were documented bear encounters at 56 out of 88 remote stations and 6 out of 85 nonremote stations. Considering bear‐disrupted sites—such as dug‐up cables or outages—there were 29 cases at remote stations and one case at nonremote stations. We also compile bear encounters with permanent stations within the Alaska Seismic Network, as well as stations of the Alaska Transportable Array. For these two networks, the stations are designed with fiberglass huts that house and protect equipment. Data losses at these networks because of bears are minor (<5%), though evidence suggests they are regularly visited by bears, and data disruptions are exclusively at remote stations. The primary goal of this study is to formally document the impacts of bears on seismic stations in Alaska and northwestern Canada. We propose that the threat of damage from bears to a station increases with the remoteness of the site and the density of bears, and it decreases with the strength and security of materials used. We suggest that low‐power electric fences be considered for seismic stations—especially for temporary experiments—to protect the equipment and to protect the bears. With the goal of 100% data returns, future seismic experiments in remote regions of bear country should carefully consider the impacts of bears.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".