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Record W3043940899 · doi:10.1029/2020gl088947

Coseismic Uplift of the 1999 <i>M</i><sub>w</sub>7.6 Chi‐Chi Earthquake and Implication to Topographic Change in Frontal Mountain Belts

2020· article· en· W3043940899 on OpenAlexaff
Ray Y. Chuang, Chih‐Heng Lu, Ci‐Jian Yang, Ya-Shien Lin, Tsung‐Yu Lee

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsFuture Earth
FundersMinistry of Science and Technology, Taiwan
KeywordsGeologyLandslideSeismologyInterferometric synthetic aperture radarSlip (aerodynamics)Earthquake ruptureGeomorphologySynthetic aperture radarRemote sensingFault (geology)

Abstract

fetched live from OpenAlex

Abstract Large dip‐slip earthquakes have a major contribution to mountain building while earthquake‐induced landslides lower mountains simultaneously. The amount of the coseismic uplift and landslides may dominate long‐term mountain evolution. However, how earthquakes contribute to mountain evolution through coseismic uplift and landslides is less constrained in real cases. We present the regional coseismic uplift of the 1999 Mw7.6 Chi‐Chi earthquake by using synthetic aperture radar (SAR) images and GPS. The coseismic uplift pattern is consistent with field observations showing increasing movement to the north with ~8 m of uplift toward the northern end. We estimated uplifted rock volume of 2.60 ± 1.09 km3, which is five times greater than the coseismic landslide volume. Intense erosion of the Taiwan orogen may erode elevated rocks rapidly, but the uplift and landslide distributions do not match and correlate more inversely, suggesting the frontal orogenic topography should be increased rather than annulled over earthquake cycles.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.035
GPT teacher head0.268
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2020
Admission routes1
Has abstractyes

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