Bibliographic record
Abstract
Receiver function analysis is useful for studying relative variations of seismic discontinuities at the lithospheric scale. This study uses receiver functions computed from a densely spaced 2D grid of six receivers that collected passive seismic data over a decade in the lower St. Lawrence River, Quebec, Canada. The lower crustal structure and lithospheric mantle are not well constrained in this study area, which is centered on the tectonic boundary between the Grenville and Appalachian provinces. Thus, the goals of this study are: 1) to establish the consistency and resolution limits of receiver functions from a large data set and dense permanent array of receivers; and 2) to use this grid to identify the geophysical Moho and describe the lithospheric mantle-to-crust transition across the Appalachian front (AF), the western boundary of Appalachian deformation. The relative seismic velocity changes under the AF resolved by Receiver Function Analysis provide evidence of local variability in the Moho’s depth and sharpness. Frequency-based analysis of the receiver functions in the northwest region of the study area produces variable Moho depth estimates from 50 to 35 km, and exhibits a gradational transition from the crust to the mantle. In the southeast region, the Moho has more consistent depth and is sharper. The likely cause of this variability is either deep-reaching shear zones that offset the Moho, or a high-velocity layer in the lower crust that is only apparent in areas where it produces significant impedance contrasts between layers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".