Procedures for seismic event type discrimination at the Canadian Hazards Information Service
Bibliographic record
Abstract
Within a catalogue of seismic events, it is necessary to distinguish natural tectonic earthquakes from seismic events due to human activity or other natural processes. This becomes very important when the data are incorporated into models of seismic hazard, since natural and anthropogenic events follow different recurrence and scaling laws. This document outlines a two-step procedure whereby first, a most likely event type is identified, and second, confirmation or refutation is sought. The procedure is intended to be compatible with current and past practices at the Canadian Hazards Information Service and the Geological Survey of Canada in assigning event types in the National Earthquake Database (NEDB). Furthermore, this document presents a new nomenclature and coding system for event types and their certainty, one that is compatible with QuakeML. Detailed classification criteria are given for all common event types; for rare event types, only definitions and examples are given.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".