Bibliographic record
Abstract
The story of “Multichannel analysis of surface waves to map bedrock” was based on a project of opportunity. Surface waves have always been the bane of near-surface reflection seismologists, even more so than petroleum exploration seismologists because of the close offsets and small two-way traveltimes we routinely deal with. With the development of MASW at the Kansas Geological Survey in the mid-1990s, surface waves have proven their utility as signal rather than noise on multichannel seismograms used for many near-surface applications. Extending the original 1D velocity estimation method to a 2D imaging and mapping technique was first demonstrated in this article. Prior to this paper, the utility, accuracy, and precision of the newly developed MASW method to estimate 1D shear-wave velocity functions had been demonstrated in several studies and publications. One of the most significant of these studies was a field test in the Vancouver, Canada area, orchestrated by Jim Hunter of the Geological Survey of Canada (which included the first blind test of the method with ground truth). This test turned out to be both the blue-ribbon success this method needed to enhance its credibility and a credit to Hunter's insight and vision. The success of that test spurred the team that authored this paper to extend the potential of this 1D method of estimating shear-wave velocities into a large-scale 2D imaging technique specifically designed for near-surface problems.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".