The Volume and Rare Earth Concentrations of Magmas Generated during Finite Stretching of the
Bibliographic record
Abstract
We present a model of lithospheric stretching and associated melting to predict the volume and rare earth element composition of basaltic magmas generated during rifting. The model differs from previous approaches in two ways: (1) we assume a two-layer lithospheric stretching model in which the amount of stretching in the crust and lower lithosphere may differ; (2) we allow for multiple instantaneous episodes of extension to occur over a specified time interval. Melt volumes are computed from the syn- and post-rift subsidences that account for the thickness of the sedimentary sequence. The concentrations of rare earth elements are calculated using an incremental melting model incorporating variations of the melt fraction with depth. The predicted melt thickness and rare earth concentrations are most sensitive to variations in the basal mantle temperature, magni-tude ofsubcrustal stretching, and time dependence of deforma-tion. Given moderately high mantle temperatures (> 1450°C) and large amounts of stretching in the upper mantle, the model is capable of generating alkaline melts, and predicts the transi-tion to tholeiitic magmatism observed in many continental rifts. We compare some of the model predictions with observations on the igneous and tectonic history of the North Sea Rift and Labrador margin (eastern Canadian margin). KEY WORDS: rifting; partial; melting; basalt; rare earth
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".