Bibliographic record
Abstract
Surface processes and magmatism condition the structural evolution of continental rifts and passive margins through mechanical and thermal effects on the lithosphere rheology. However, their inter-relationships in extensional settings are largely unknown. Here, I use coupled thermo-mechanical geodynamic and landscape evolution numerical modeling to assess the links between erosion of rift shoulders, sedimentation within the rift basin and extensional rock melting. Results suggest that, when the crust is thinner than ~40 km, the extension rate is slower than ~2 cm/yr and the mantle potential temperature is below ~1230 °C, efficient surface processes may double crustal melting by Moho lowering and inhibit mantle decompression melting by ~50% through sediment loading within the rift basin. It is thus likely that surface processes significantly influenced the magmatic activity of a number of extensional settings worldwide - e.g. the Mediterranean, the Gulf of California, the Iberia-Newfoundland margin, and the South China Sea. Because magmatism and surface processes affect jointly the geological carbon cycle, the surface processes forcing on extensional rock melting investigated here involves an additional means of linkage between plate tectonics and climate changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".