TOQUES: An Ocean-Bottom Seismometer Dataset on the Central Queen Charlotte Fault, Southeast Alaska
Bibliographic record
Abstract
Abstract Plate boundaries in the oceans are often poorly monitored. Though typically less remote than the deep sea, shallow marine environments with seafloor depths <0.5 km can be especially challenging for seismic experiments due to natural and anthropogenic hazards and noise sources that can affect instrument survival and data quality. The Queen Charlotte fault (QCF) is part of a transform plate boundary that follows the continental shelf of the Alaska Panhandle and central British Columbia. This fault system accommodates dextral slip between the Pacific and North American plates and has hosted several historic Mw > 7 earthquakes. In August 2021, we deployed 28 broadband ocean-bottom seismometers (OBSs) along the central QCF for the “Transform Obliquity along the Queen Charlotte Fault and Earthquake Study” (TOQUES) to investigate fault architecture and local seismicity. Deployment depths varied between 0.2 and 2.5 km below sea level, with half of the instruments deployed in shallow water (<0.5 km depth). We describe the scientific motivations for the TOQUES broadband OBS array, present data metrics, and discuss factors that influence data quality and instrument survival. We show that many opportunities exist for scientific study of shallow marine environments and the solid earth. Despite concerns that shallow water was responsible for the risk of data or instrument loss, direct relationships between instrument success and water depth are inconclusive. Rather, instrument success may be more related to the ability of different instrument designs to withstand shallow-water conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".