Evolution of deep‐water rifted margins: Testing depth‐dependent extensional models
Bibliographic record
Abstract
A general understanding of rifted margins, which form by thinning of the continental lithosphere, exists. Nevertheless, the exact form of thinning is unclear. This debate has been stimulated by acquisition of dense seismic wide‐angle and deep reflection surveys from Atlantic Ocean margins. A central issue concerns the way in which thinning changes with depth. We have tackled this issue by developing a generalized inverse model. This model attempts to fit subsidence and crustal thinning observations by varying strain rate as a function of time and space. Depth‐dependent thinning is permitted but we do not prescribe its existence or form. Here, the algorithm is applied to six margins, including two of the most contentious conjugate margins: Newfoundland‐Iberia and Brazil‐Angola. Calculated strain rate histories predict thinning estimates which broadly match estimates inferred from normal faulting. The Eastern Indian and Beaufort Sea margins formed by largely uniform lithospheric thinning. In contrast, the Newfoundland‐Iberian conjugate margins formed by a pattern of strongly depth‐dependent strain rate. To account for the paucity of syn‐rift decompression melting of the underlying asthenosphere, the lithospheric mantle close to oceanic‐continent transition must thin more slowly than the overlying crust. This form of depth dependency is not common. For example, the Brazil‐Angolan conjugate margin could have formed by uniform lithospheric thinning provided thick layers of salt were deposited in a preexisting 400 m deep topographic depression. Depth‐dependent thinning is not required to account for rapid subsidence of presalt strata.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".