Performance comparison between current automated earthquake location methods, Autoloc and Antelope
Bibliographic record
Abstract
The two methods of automatic earthquake location currently in use (as of May 2010) by the Canadian Hazards Information Service, Autoloc and Antelope, are compared to solutions produced daily by seismologists in an effort to evaluate the performance of both algorithms. The one year evaluation period extends from May 7, 2009 to May 6, 2010. Results show that both algorithms have their strengths and weaknesses that are specific to their current usage. In general Autoloc outperforms Antelope in terms of earthquake event detection across magnitudes of 2.0 and greater, while Antelope appears to possess a superior ability to identify seismic phase arrival times and thus is more accurate in its automated locations of earthquakes. Currently magnitude assessment is difficult for Antelope due to limitations in its design, however, for Autoloc current overall automated magnitudes for earthquakes >2.0 are underestimated by -0.3 magnitudes, improving to 0.04 as magnitude increases to 4.5. Potential exists in combining the strengths of both algorithms either as a single entity or by using both in tandem for improved hazard awareness.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".