Peace River Induced Seismic Monitoring (PRISM) Nodal Seismic Array
Bibliographic record
Abstract
Abstract From 23 November 2022 to 30 November 2022, a sequence of earthquakes with a peak magnitude of ML 5.6 occurred ∼46 km away from Peace River—a vibrant rural community in Alberta, Canada. Broadly felt by residents throughout central Alberta, the ML 5.6 earthquake on 30 November 2022 registers as the second-largest earthquake ever reported in the Western Canada Sedimentary basin and possibly the largest Canadian earthquake induced by human activities. On 6 December 2022, 1 week after the mainshock, the University of Alberta and Alberta Geological Survey jointly installed a circular array of nodal geophones surrounding the seismogenic zone. Over the next 4 months, this quick-response array (nicknamed “Peace River Induced Seismic Monitoring” array, for short PRISM) operated at temperatures as low as −30°C and substantially bolstered the seismic data coverage in this previously undersampled region. Our preliminary array data analysis has detected more than 2000 earthquakes with magnitudes ranging from −1.9 to 5.0 since the initial outbreak in late 2022. Investigations based on earthquake location, focal mechanism, and magnitude jointly reveal distinct earthquake clusters distributed along pre-existing faults from earlier tectonic events. The data recovered from this array offer unique and vital constraints on the tectonic histories and seismic risks of the Peace River region.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".