Crustal properties beneath the northeastern United States shaped by past tectonic processes
Bibliographic record
Abstract
Constraints on the thickness, transitional boundaries, and composition of Earth's crust are pivotal in studying its formation and evolution. We use data from 132 seismic installations throughout the northeastern US to explore how tectonic events, such as orogenesis and rifting, have altered the crust of the northeastern US and southeastern Canada, and to distinguish between Laurentia and the Appalachian terranes. We include data from seismic installations from the NEST and SEISConn experiments, spanning the Laurentia–Appalachian boundary, and present estimates of crustal thickness, V p / V s , and thickness of the transition between crustal and mantle rocks using Ps receiver functions. We find some first-order differences between Laurentia and Appalachian terranes, with Laurentia exhibiting thicker crust ( c. 39 v. c. 33 km) and a broader crust–mantle transition thickness ( c. 3 v. <1.5 km). Average V p / V s values are similar between Laurentia ( c. 1.77) and Appalachian terranes ( c. 1.74); however, we identify anomalous V p / V s in a few regions, including high V p / V s around the Adirondack Mountains and low V p / V s in southern New England. The southern New England region is also anomalous in terms of its systematically thinner crust and sharper crust–mantle transition, which may be a consequence of the formation and collapse of the Acadian altiplano during the mid-to-late Paleozoic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".