Source model inputs and results - The July 2022 Mw 7.0 Northwestern Luzon Earthquake, Philippines
Bibliographic record
Abstract
This repository includes all the modelling inputs necessary to reproduce the results presented in 'Source Model and Characteristics of the 27 July 2022 MW 7.0 Northwestern Luzon Earthquake, Philippines' by Rimando et al. (2022) as follows: the fault geometries ('custom_fault_dip30_vaf,' 'custom_fault_dip30_abra'), the downsampled InSAR LOS deformation input ('statics'), the crustal model ('crust01'), and the run file which includes all the run parameters that were used ('luzon_run_clean'). Also included are the main outputs ('Abra_Results' and 'Vigan_Results') that Mudpy should produce using the abovementioned input files. Once an interested party downloads MudPy (MudPy v.1.0 was used for this study: https://github.com/dmelgarm/MudPy), these folders just have to be placed in their spots in the directory structure (outlined at https://github.com/dmelgarm/MudPy/wiki/) in order to reproduce the findings in Rimando et al. (2022).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.046 |
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".