Bibliographic record
Abstract
OrientPy is a toolbox to help determine seismometer orientation using automated (and manual) processing of earthquake data. These methods are particularly useful for broadband ocean-bottom seismic stations, but are also applicable to broadband land stations or shorter period instruments (depending on the method selected). The code uses the <code>StDb</code> package for querying and building a station database and can be used through command-line scripts. Currently the toolbox includes the following methods: DL (Doran and Laske, 2017): Based on Rayleigh-wave polarization at a range of frequencies and for the two fundamental mode Rayleigh wave orbits. BNG (Braunmiller, Nabelek and Ghods, 2020): Based on P-wave polarization from regional and teleseismic earthquakes. LKSS (Lim et al., 2018): Based on the harmonic decomposition of radial and transverse receiver functions near zero lag times. Each method can be used independently to produce an estimate of station orientation, in terms of the azimuth of seismic component <code>1</code> (or <code>N</code>).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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; both teacher heads agree on what is shown here.
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".