Understanding the Northern Canadian Shield: Moho Depth, Anisotropy, and Tectonics from Receiver Functions
Bibliographic record
Abstract
While extensively studied and well understood in certain regions, the Canadian Shield still presents significant challenges and uncertainties in its northern areas, particularly within the Churchill Province. Two major unresolved aspects in this region are the precise determination of Moho depth and the characterization of anisotropy.To address these gaps, this study employs DeepRFQC, a state-of-the-art technique for rigorous quality control of receiver function signals. By analyzing all available signals across the area, DeepRFQC ensures that only high-quality data are selected, providing a robust foundation for subsequent analyses.We used H-k stacking and Harmonic Decomposition to investigate the lithospheric structure. Calculated kappa (κ) values showed strong agreement with gravity data, underscoring the reliability of the results. Harmonic Decomposition revealed anisotropy patterns consistent with the regional stress field, offering new insights into the tectonic processes.These findings suggest that the observed seismic anisotropy is closely aligned with the region’s predominantly NE-SW geological structures, providing a clearer understanding of the Churchill Province's lithospheric dynamics.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".