A tri-stage cluster identification model for accurate analysis of seismic catalogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. In this paper we propose a tri-stage cluster identification model that is a combination of a simple single iteration distance algorithm and an iterative K-means algorithm. In this study of earthquake seismicity, the model considers event location, time and magnitude information from earthquake catalog data to efficiently classify events as either background or mainshock and aftershock sequences. Tests on a synthetic seismicity catalog demonstrate the efficiency of the proposed model in terms of accuracy percentage (94.81% for background and 89.46% for aftershocks). The close agreement between lambda and cumulative plots for the ideal synthetic catalog and that generated by the proposed model also supports the accuracy of the proposed technique. There is flexibility in the model design to allow for proper selection of location and magnitude ranges, depending upon the nature of the mainshocks present in the catalog. The effectiveness of the proposed model also is evaluated by the classification of events in three historic catalogs: California, Japan and Indonesia. As expected, for both synthetic and historic catalog analysis it is observed that the density of events classified as background is almost uniform throughout the region, whereas the density of aftershock events are higher near the mainshocks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it