On the determination of a global strain rate model
Bibliographic record
Abstract
The objective of this paper is to outline the fundamental concepts underlying the estimation of a global strain rate model. We use a variant of the method first introduced by Haines and Holt (1993) to estimate the strain rate tensor field within all of the Earth’s deforming regions. Currently the observables used are ~1650 geodetic velocities, seismic moment tensors from the Harvard CMT catalog, and Quaternary fault slip rate data. A model strain rate field and velocity field are obtained in a least-squares fit to both the geodetic velocities and the observed strain rates inferred from fault slip rates. Seismic moment tensors are used to provide a priori constraints on the style and direction (not magnitude) of the model strain rate field for regions where no fault slip rate data are available. The model will soon be expanded to include spreading rates, ocean transform azimuths, and more fault slip rate data. We present a first estimate of the second invariant of the global model strain rate field. We also present Euler poles obtained by fitting geodetic vectors located on defined rigid plates. We find that 17% of the total model moment rate is accommodated in zones of (diffuse) continental deformation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".