Space Geodetic Data Improve Seismic Hazard Assessment in California: Workshop on Incorporating Geodetic Surface Deformation Data Into UCERF3; Pomona, California, 1–2 April 2010
Bibliographic record
Abstract
A workshop was held to begin scientific consideration of how to incorporate space geodetic constraints on strain rates and fault slip rates into the next generation Uniform California Earthquake Rupture Forecast, version 3 (UCERF3), due to be completed in mid‐2012. Principal outcomes of the meeting were (1) an assessment of secure science ready for UCERF3 applications within the next year, and (2) an agenda of new research objectives for the Southern California Earthquake Center (SCEC), the U.S. Geological Survey (USGS), and others in support of UCERF3 and related probabilistic seismic hazard assessments (PSHA). A number of goals potentially achievable within a year were identified, including (1) slip rate and fault locking depth estimates, with uncertainties or ranges, for all major and some minor faults of the extended San Andreas system; (2) strain rate estimates or bounds on rates for selected regions lying off the major faults of the San Andreas system; and (3) corrections or bounds on perturbing effects of postseismic deformation and elastic modulus heterogeneities on the observed Global Positioning System (GPS) velocity field (needed as input to models for estimating fault slip and strain rates in goals 1 and 2 above).
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".