Advances in Deformable Plate Tectonic Models: 1. Reconstructing Deformable Continental Blocks and Crustal Thicknesses Back Through Time
Bibliographic record
Abstract
Abstract Deformable plate tectonic models have been demonstrated to be a useful technique for quantifying temporal variations in strain rate and crustal thickness within recent plate kinematic studies. Using the GPlates software, deformable plate models offer an approach to visualize and assess the interplay of plate kinematics and deformation. However, several assumptions are imposed in previous studies that limit their ability to explain the crustal evolution of various tectonic regimes. Examples of these assumptions include, but are not limited to, the rigid nature of continental blocks and boundaries used to define deformable regions, and uniform crustal thickness assumptions at model start times. In this study, we address these assumptions with newly presented applications using the interplay of GPlates and its python programming library, pyGPlates. In particular, we demonstrate the ability to create deformable continental blocks, reconstruct present day crustal thickness estimates back through time, and how the landward extent of present day crustal thickness estimates can be used to define the limits of deformable plate models and rift domain boundaries a priori. To demonstrate their application and validity, these concepts are evaluated using a previously published deformable plate model of the southern North Atlantic that is tested using 4 modeling scenarios herein to assess the impact of variable model inputs. These models provide insight regarding the pre‐Jurassic (200 Ma) crustal thickness template of the southern North Atlantic, the evolution of continental blocks during rift‐related deformation, and the potential impact of ancient orogenic terranes during subsequent rifting within the North Atlantic.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".