A recipe for continental fragment formation: big data analysis of rift models
Bibliographic record
Abstract
Former plate boundaries (sutures) are usually considered to be future locations for continental breakup, but this is not always the case. For example, continental rifting can generate a crustal fragment, where a sliver of a plate diverges from its component part and remains attached to another plate. Despite the prevalence of continental fragments and accreted terranes in the geological record, the underlying tectonic processes leading to their formation remain poorly understood. Previous geodynamic models have indicated structural and rheological heterogeneities inherited from past tectonic events as a key mechanism driving the initiation of continental breakup. Most of these studies have primarily focused on the styles of rifted margins, but limited attention is given to the mechanism of continental fragment formation.In this study, we present a suite of over 100 different numerical models of inherited structures with the tectonic potential to generate a new continental fragment during continental extension. Our models show the first-order impact of structural inheritance on the evolution of rifting and continental fragmentation. Here, the size of the fragment is influenced by the extent and geometry of the inherited structures. By analyzing our models using novel data science techniques, we are able to quantify the impact of different initial conditions on generating a continental fragment. Our models provide a range of new physical constraints for the formation of continental fragments. Most importantly, they highlight the potential role of different forms of structural inheritance in controlling deformation within complex tectonic plate margins. Finally, we apply these findings to some real-world examples of continental fragments.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".