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Record W4399125139 · doi:10.59236/geomorphica.v1i1.28

Morphometric Evaluation of the Relative Uplift Rates along the Vigan–Aggao Fault in Ilocos Norte, Philippines

2024· article· en· W4399125139 on OpenAlexaff
Ace Matthew Fajardo Cantillep, Noelynna T. Ramos, Jeremy Rimando

Bibliographic record

VenueGeomorphica. · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsUniversity of TorontoCanadian Nuclear Safety Commission
Fundersnot available
KeywordsGeologyFault (geology)GeographySeismology

Abstract

fetched live from OpenAlex

The Vigan–Aggao Fault (VAF) in northern Luzon Island is a NNE-trending sinistral fault divided by fault bends, which are associated with local variations in the fault’s kinematics. In this study, we examined the relative uplift rates across the fault bend in the San Juan–Vintar segment of the VAF using morphometric indices. Basin-based indices, namely hypsometric integral, basin shape, basin elongation ratio, and basin asymmetry factor, and non-basin-based indices, namely stream length-gradient index, normalized stream length-gradient index, and mountain front sinuosity were calculated to isolate and examine the surface processes that influence landscape development. We then integrated the basin-based results with geological data to create a relative tectonic activity index (RTAI). Clustering analysis of the results revealed hotspots along the bent section of the fault indicating more values that suggest higher relative uplift rates during the development of the landscape. The morphometric indices showed that the highest uplift rates are along the central VAF strands. This study described and evaluated the relative uplift rates of a known active fault system in Ilocos Norte, in the absence of detailed field structural data. This study also reinforces the utility of morphometry in identifying priority sites for detailed paleoseismic and seismic hazard analyses.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.252
Teacher spread0.216 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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