Point estimates of Crustal thickness Using receiver Function Stacking
Bibliographic record
Abstract
Introduction: Through receiver function analysis, this study inquires into some of the most basic properties of the crust below southern and central Quebec. Methods: This is accomplished, using receiver function technique, by stacking waveforms from 277 teleseismic events magnitude 6.0 and larger to find the delay in arrival time for several phases of the p-wave coda, relative to the initial p-wave arrival. This information is used to establish a linear relationship between thickness and p- to s-wave velocity ratio, each of which is stacked for a given station to identify a best-fit estimate for depth to the moho and Vp/Vs ratio. To determine their accuracy these results are compared with previous seismic studies, as well as synthetically generated receiver function p-wave arrivals based on simple 1d crustal models. Thickness calculations for the crust varied from 28 to 48 km; variation which was most likely the result of either complicated 3d structures or a shortage of available high-magnitude events for some stations. most of the results fell within appropriate windows outlined by studies like liTHoprobe. discussion: given that the 9 broadband stations used in this study compose an area from the superior province, grenville province and a selection of their subprovinces and intrusions, reliable evaluations of the crustal thicknesses below these seismic stations have broad relevance in understanding the crustal structure below Quebec.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".