Obtaining Unique, Comprehensive Deep Seismic Sounding Data Sets for CTBT Monitoring and Broad Seismological Studies
Bibliographic record
Abstract
In cooperation with the Center for Geophysical and Geoecological Studies (GEON, Moscow, Russia), the University of Wyoming and now also with the University of Saskatchewan digitized, edited, transferred into standard digital formats, and delivered to public domain seismic records from 12 major Deep Seismic Sounding (DSS) projects acquired in 1970-1980's in the former Soviet Union. The data include 3-component records from 22 Peaceful Nuclear Explosions (PNEs) and over 500 chemical explosions recorded by a grid of linear, reversed seismic profiles covering a large part of Northern Eurasia. Digital copies of all records were delivered to AFRL and to the Incorporated Research Institutions for Seismology Data Center for unrestricted distribution to researchers. The availability of the DSS PNE datasets resulting from this project, combined with the recent results arising from them (velocity, reflectivity, Receiver Functions, mantle attenuation, Lg Q, P- and Lg coda Q, scattering, phase amplitude ratios, empirical first-arrival travel times) makes the area of PNE profiling one of the best-constrained seismically at short periods, both for structural studies and for nuclear test monitoring.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".