Resolving Quaternary Tectonic Activity with High-Resolution Data in Space and Time
Bibliographic record
Abstract
Abstract Large earthquakes are among the most dangerous natural disasters with potentially devastating effects on society and infrastructure across the globe. In order to better understand earthquakes, research in active tectonics aims at quantifying crustal deformation throughout the active fault’s earthquake cycles by studying geomorphic and stratigraphic evidence of recent and past earthquakes. The underlying assumption in this approach is that a fault’s current and previous seismic behavior is representative of its future behavior. Constraining a fault’s seismic behavior in such a manner requires high-resolution geomorphic and stratigraphic records that enable us to resolve the spatial and temporal characteristics of co-, post-, and interseismic phases, ideally over multiple earthquake cycles. Recent technological developments have dramatically increased not only the amount and resolution of topographic and geophysical survey data sets but also our ability to date stratigraphic units and geomorphic surfaces. These technological advances have enabled us to better understand the interplay between crustal deformation, earthquake ruptures, and their signature in geomorphic and stratigraphic records. In particular, the availability of high-resolution data sets from LiDAR, SfM, or geophysical surveys and the use of accurate dating methods such as cosmogenic or OSL dating allow us to quantitatively study surface deformation at high spatial resolution over large areas and at multiple time scales—from a few years to millions of years. In this special issue, we focus on the tectonic activity of active faults and the geomorphic processes in various tectonic regimes worldwide. It covers active tectonics, earthquake geology, remote sensing, tectonic geomorphology, Quaternary geochronology, geohazard, and seismology.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".