Reexamination confirming additional seismic evidence for the 12 May 2010 low-yield nuclear test
Bibliographic record
Abstract
This study examines event location and source discrimination for the May 12, 2010 possible nuclear event in North Korea, using seismic data recorded in the Dongbei Broadband Seismic Network in China. Using highly similar Pg waveforms between the events, we locate the event within 2 km of the 2009 nuclear test site. For event classification, we first build a group of reference earthquakes and explosions through a machine-learning phase picker and an event association/location process, with open-access satellite image verification; we then classify the event based on the Pg/Lg spectral ratios and Pg waveform similarity comparisons with past nuclear tests. The event is classified as a small explosion reaffirming the findings of Zhang and Wen (2015a). Along with independent scientific evidence from radionuclide studies and infrasound signal analysis, the May 12, 2010 event can be reasonably attributed to a low-yield nuclear test. Our study shows that explosion discriminants are dependent on event magnitudes and seismic noises , and vary from station to station. Accordingly, event classification should be performed on a station-by-station basis. The approach presented here is useful for seismic monitoring of low-yield nuclear tests.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".