Potential effect of seasonally varying station thresholds on joint-maximum-likelihood magnitude estimates from bulletin data 
Bibliographic record
Abstract
Accurate seismic body-wave magnitudes (mb) are important in nuclear test-ban treaty verification. Network mean magnitudes are known to be biased when the effect of noise obscuring signal at some stations in the monitoring network is ignored. To overcome this bias a joint-maximum-likelihood method is used to invert bulletin amplitude and period measurements at a network of stations from a number of closely spaced sources, to estimate unbiased network mb values and station corrections. For each station a noise threshold is determined independently using the Kelly & Lacoss (1969) method, assuming that large samples of amplitudes reported in a bulletin (in this case from the International Seismological Centre, ISC) follow a Gutenberg-Richter distribution. Where stations report arrivals sufficiently frequently, the noise threshold can be estimated separately for different seasons, to highlight variations caused by, for instance, storms or freezing of nearby ocean. The noise thresholds at some stations differ by up to 0.4 magnitude units between seasons. Sensitivity of maximum-likelihood magnitude estimates of a group of announced explosions at the Nevada Test Site to variations in threshold at Canadian Arctic stations (compared with using the annual mean) is generally small (<∼0.01-0.02 units), and greatest for low-magnitude events in the “noisy” season, when the station magnitudes are below the seasonal threshold but above the annual average threshold. UK Ministry of Defence © Crown copyright 2021/AWE
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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 | 0.029 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".