On the Efficiency of the Multi-Channel Analysis of Surface Wave Method for Shallow and Semi-Deep Loose Soil Layers
Bibliographic record
Abstract
The multi-channel analysis of surface waves (MASWs) method was used to obtain the shear wave velocity variations through near surface (depth < 30 m) and semi-deep (30 m < depth < 100 m) soil layers in the city of Ottawa, Canada. Sixteen sites were examined to evaluate the capability of the active and passive MASW methods for cases where the shear wave velocity<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math>contrast between very loose soil (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mrow></mml:mrow></mml:math>< 200 m/s) and very firm bedrock (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mrow></mml:math>> 2,300 m/s) is very large. The MASW velocity results compared with those of other geophysical approaches, such as seismic reflection/refraction methods and borehole data, where available, mostly confirming the capability of the MASW method to distinguish the high shear wave velocity contrast in the study area. We have found that, of the inversion procedures of MASW data, the random search inversion technique provides better results than the analytical generalized inversion method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".