Source Model and Characteristics of the 27 July 2022 MW 7.0 Northwestern Luzon Earthquake, Philippines
Bibliographic record
Abstract
The geometry and kinematics of the causative fault of the 27 July 2022 moment magnitude (Mw) 7.0 earthquake, which is one of the strongest to hit northern and central Luzon in the past 30 years, were estimated through inverse modeling of line-of-sight interferometric synthetic aperture radar deformation. We modeled rupture along multiple candidate faults based on fit with the pattern of line-of-sight deformation, consistency with focal mechanisms, and compatibility with the known kinematics of the mapped active faults in the region. Our preferred fault model, located west of and parallel to the Abra River Fault (ARF), exhibits localized reverse-slip (average 67° rake) at 15-35 km down-dip. Peak slip occurs at 13-16 km depth, with 95 cm of pure reverse-slip. The existence of a reverse-slip dominated ARF-parallel fault rupture is consistent with a complex shear partitioning model, wherein the NW-SE oblique plate convergence is accommodated not only by the sinistral strike-slip Philippine Fault Zone and the major subduction zones, but also by minor faults in intervening crustal blocks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".