Baffin Island Broadband Offshore Seismology
Bibliographic record
Abstract
The Baffin-Labrador Seaway is the product of continental rifting and seafloor spreading between Greenland and the eastern Canadian Arctic. Although rifting has ceased, it has left dense networks of faults that host abundant seismicity today. In northwestern Baffin Bay, ~1300 earthquakes of M>3 have been recorded over the past 30 years by the land-based CNSN network. These earthquakes pose hazard to communities on the northeastern coast of Baffin Island and western Greenland. The remoteness and vast scale of this area has historically made it extremely challenging to survey with seismic or acoustic instruments, leaving it poorly characterized in many contexts. The BIBOS project seeks to improve understanding of the geodynamic processes in northern Baffin Bay, including characterizing the tectonic stress field, locations/dimensions of potentially seismogenic faults, intraplate earthquake recurrence intervals and large-scale crustal structure. This information will be used to improve seismic hazard assessments in the eastern Canadian Arctic. OBS data can also be used to characterize the underwater acoustic environment, which has become increasingly important for both military and civilian applications in the region. A network of 28 Aquarius ocean-bottom seismometers (OBS) from the National Facility for Seismological Investigations (Dalhousie University) was deployed from September 2024 through October 2025 in the western half of northern Baffin Bay. The array covers an area of notable seismicity between the coast of Baffin Island and the dormant rift structure which runs approximately down the center of the bay. This project is a collaborative effort involving scientists from Dalhousie University, the Geological Survey of Canada and Defense Research and Development Canada (DRDC), and forms part of the larger ICE-OBS (Investigating Canada's Eastern Offshore with Broadband Seismology) initiative to study the Canadian Atlantic and Arctic offshore regions.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".