Morphotectonic Kinematic Indicators along the Vigan-Aggao Fault: The Western Deformation Front of the Philippine Fault Zone in Northern Luzon, the Philippines
Bibliographic record
Abstract
The Vigan-Aggao Fault is a 140-km-long complex active fault system consisting of multiple traces in the westernmost part of the Philippine Fault Zone (PFZ) in northern Luzon, the Philippines. In this paper, its traces, segmentation, and oblique left-lateral strike-slip motion are determined from horizontal and vertical displacements measured from over a thousand piercing points pricked from displaced spurs and streams observed from Google Earth Pro satellite images. This work marks the first instance of the extensive use of Google Earth as a tool in mapping and determining the kinematics of active faults. Complete 3D image coverage of a major thoroughgoing active fault system is freely and easily accessible on the Google Earth Pro platform. It provides a great advantage to researchers collecting morphotectonic displacement data, especially where access to aerial photos covering the entire fault system is next to impossible. This tool has not been applied in the past due to apprehensions on the positional measurement accuracy (mainly of the vertical component). The new method outlined in this paper demonstrates the applicability of this tool in the detailed mapping of active fault traces through a neotectonic analysis of fault-zone features. From the sense of motion of the active faults in northern Luzon and of the major bounding faults in central Luzon, the nature of deformation in these regions can be inferred. An understanding of the kinematics is critical in appreciating the distribution and the preferred mode of accommodation of deformation by faulting in central and northern Luzon resulting from oblique convergence of the Sunda Plate and the Philippine Sea Plate. The location, extent, segmentation patterns, and sense of motion of active faults are critical in coming up with reasonable estimates of the hazards involved and identifying areas prone to these hazards. The magnitude of earthquakes is also partly dependent on the type and nature of fault movement. With a proper evaluation of these parameters, earthquake hazards and their effects in different tectonic settings worldwide can be estimated more accurately.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".