Improved Green's functions for passive-source structural studies
Bibliographic record
Abstract
Abstract Over the past two decades, teleseismic receiver functions have proved to be a useful tool to investigate crustal structure. Because they represent a first-order approximation to the S-wave component of the teleseismic-P Green's function, receiver functions provide valuable information on physical properties related to shear modulus. However, the implicit use of the P-component seismogram as a proxy for the source precludes the recovery of information on discontinuous structure involving contrasts in compressional modulus. By deconvolving improved estimates of complex source time functions generated by earthquakes, one may move beyond the conventional receiver function paradigm to a more accurate approximation of the earth's Green's function. Using a new deconvolution method, we present estimates of the P-component of the teleseismic-P Green's functions at several stations of the Canadian National Seismic Network (CNSN) that clearly show the receiver-side pure P-wave crustal multiple. The identification and characterization of these signals in studies of the lithosphere will afford better constraints on subsurface lithology and represent a narrowing of the gap between active- and passive-source seismic imaging.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".