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Record W2891832042 · doi:10.4138/atlgeol.2018.009

Investigation of the 1727 Newbury, Massachusetts, USA, earthquake using LiDAR imagery and P-wave velocity tomography

2018· article· en· W2891832042 on OpenAlexvenueno aff
Ronald T. Marple, James D. Hurd, Lanbo Liu, Seth Travis, Robert J. Altamura

Bibliographic record

VenueAtlantic Geology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
Fundersnot available
KeywordsLineamentGeologyFault scarpSeismologyRidgeLidarFault (geology)Thrust faultVolcanoGeomorphologyTectonicsRemote sensingPaleontology

Abstract

fetched live from OpenAlex

High-resolution LiDAR (light detection and ranging) images of northeastern Massachusetts and southeastern New Hampshire reveal a 10-km-long, NW-SE-oriented topographic lineament in northeastern Massachusetts that we interpret to be the surface expression of a SW-dipping thrust fault along which the 1727 Newbury, Massachusetts, earthquake occurred. The Newburyport lineament coincides with the northeast edge of a 10-kmlong, NW-SE-oriented ridge, herein named Merrimack ridge, that parallels the NW-SE-trending segment of the Merrimack River downstream from where it bends 90° to the southeast. The northwestern end of the Newburyport lineament coincides with a 1-km-long, ~7- to 15-m-high, NE-facing Newburyport scarp that is located just south of the bend in the river. The Newburyport lineament also parallels the NW-SE-oriented nodal planes of the focal mechanism that was generated for the 1999 Amesbury, Massachusetts, earthquake. A P-wave velocity tomographic model generated from a seismic-refraction profile across the Newburyport scarp shows a ~40-m-wide low-velocity zone dipping ~41° SW. Velocities along this zone decrease 15–50%, which suggests that the Newburyport lineament is associated with the surface expression of a SW-dipping brittle fault zone. The LiDAR images also revealed three other NW-SE-trending lineaments in the study area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.214
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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