Bibliographic record
Abstract
The current rapid growth in the number of seismometers available to the research community, combined with increasing computer power, will allow improvement in the type and quality of seismic images of the crust and lithosphere. An example of improved imaging capability is the inversion of the full seismic waveform, rather than solely travel times, in controlled‐source surveys (seismic refraction or reflection using human‐induced ground shaking). At the 12th Deep Seismic Methods workshop in 2003, sponsored by the International Association of Seismology and Physics of the Earth's Interior (IASPEI), an analysis of computer‐generated data exemplified the potential of increased source and station density. The synthetic seismic data set was generated from a geologic model that includes large‐, medium‐, and small‐scale stochastic variation. The source and seismometer spacing mimic imminent community capabilities. The Earth model was kept secret, and the data were made available for analysis (http://crust. geol.vt.edu/hole/ccss/). Figure 1 illustrates the results of blind travel time and waveform tomography applied to the data.The images, described in more detail below, illustrate an excellent match to the true Earth model.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".