Bibliographic record
Abstract
1135 Seismology has provided records of earthquake shaking dating back centuries, but even the longest historical records cannot suffi cient to demonstrate the long-term patterns, as we learned from the 2011 Tohoku Earthquake (Liu and Zhou, 2012). A major challenge for the study of past earthquakes is that events on such time scales—seconds to minutes—are rarely preserved in the rock record. When they are, the records represent isolated moments, not the days to years before and after the event that would place them in context. “Earthquake geology” includes the study of rupture itself (on the fault surface), ground shaking and its effects, and stress and fl uid pressure changes both on- and off-fault, on geologic to human time scales (Sibson, 2011). Loope et al. (2013, p. 1131 in this issue of Geology) document an exceptionally well-preserved array of sand volcanoes in the Navajo Sandstone (southwest United States), capturing an ~1-yr-long Jurassic earthquake swarm. Radiating seismic waves cause transient changes in pressure conditions that may result in permanent damage. Examples include slope failure, intrusion, injection and extrusion of fl uidized sediments (liquefaction), folding and slumping, and autobrecciation (Montenat et al., 2007). Such deformed sediments, sometimes called “seismites,” record energetic disturbance of material at the earth’s surface. If the sediments are rapidly buried, they may be preserved and identifi able in the rock record. Earthquakes, however, are not the only source of energy that might deform soft sediments. Storms, landslides and rockfalls, currents, far-traveled tsunami waves, and impacts may also cause liquefaction and deformation. The stress perturbation caused by such a transient event might be required for
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".