Bibliographic record
Abstract
SUMMARY Different data components of teleseismic waveforms are related by transfer functions that depend only on receiver-side structure. This is the common basis of a number of teleseismic techniques, including receiver functions and shear wave splitting analysis. Common trace misfits used in these analysis techniques are shown to be equivalent to band-limited comparisons of real and synthetic transfer functions. The data deconvolution used in receiver function analysis leads to reduced structural resolution compared to direct trace-based misfits such as cross-convolution, with direct transfer function modelling of a data trace having the particular advantage of a physically meaningful misfit. Having established that the intertrace transfer function contains all available structural information, the sensitivity of transfer functions to structure is examined for a series of teleseismic scenarios. Transfer functions for the teleseismic P coda show a strong sensitivity to shallow low-velocity structures such as sedimentary basins; the Sp precursors used in S receiver functions are less affected. Examination of transfer functions for shear wave splitting shows that response complexities occur at frequencies too high to be observable in teleseismic studies, and that the dominant control on the response is the splitting intensity.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".