Bibliographic record
Abstract
Abstract Edge- and tip-wave theories were developed during a time when computational power was not readily available for verification by comparing with full wave solutions. However, physical modeling of wave propagation was common in several Soviet laboratories, including the Institute of Geophysics in Novosibirsk, where the initial theory and algorithms were developed (Klem-Musatovet al., 1972, 1975, 1976, 1982; Aizenberg and Klem-Musatov, 1980; Aizenberg, 1982). The first section of this chapter reviews experiments made by Russian scientists to compare their theoretical calculations against experimental data in simple 2D and 3D models (Klem-Musatov, 1980; Landa and Maksimov, 1980; Luneva and Kharlamov, 1990). Because theory and applications of edge and tip waves were published in Western journals (Klem-Musatov and Aizenberg, 1984, 1985, 1989), several groups pursued their own implementation, e.g., Pajchel et al. (1987, 1988, 1989) in Norway, Hoffmann et al. (1993) and Klaeschen et al. (1994) in Germany, Hron and Chan (1995) in Canada, and Wang and Waltham (1995) in the United Kingdom. As ray-method applications developed as tools in geophysical prospecting, edge-wave theory was discovered to be a convenient remedy for limitations of the ray approach in handling model discontinuities. We devote the second section of this chapter to one of the first practical implementations of edge-wave theory: the 2D software package of Pajchel et al. (1987). This implementation was used widely for practical exploration problems in the North Sea, where discontinuities in geologic structures and diffractions are common features of seismic sections. Edge-wave theory fails where the ray-theory field changes rapidly
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.109 | 0.033 |
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".