Beyond ‘crumple zones’: recent advances, applications and future directions in deformable plate tectonic modelling
Bibliographic record
Abstract
Abstract The recent proliferation of deformable plate tectonic modelling techniques has provided a new direction in the study of plate tectonics with substantial implications for our understanding of plate deformation and past kinematics. Such models account for intraplate deformation, yet are highly variable in their inputs, capabilities and applications. The aim of this commentary is to review recent contributions to this topic, and to consider future directions and major omissions. Through this review it is apparent that the current published deformable models can be subdivided into those that as an input either: (1) solely use plate motions to drive deformation, or (2) require stretching or beta factor. Deformable models are resolving some outstanding issues with plate reconstructions, but major simplifications and modelling assumptions remain. Primarily, obtaining model constraints on the spatio-temporal evolution of deformation is an outstanding problem. Deformable plate models likely work best when the kinematics of smaller plates are included. However, questions remain regarding how to define such blocks, and their kinematic histories, whilst some work suggests that inclusion of such entities is negated through quantitative restorations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".