Spawning superplumes from the midmantle: The impact of spin transitions in the mantle
Bibliographic record
Abstract
Abstract The formation of large‐scale upwellings with lateral extents of several hundreds of kilometers, reaching up to ∼10,000 km or more, still remains a hotly debated topic. Some seismic imaging studies based on high‐resolution data suggest that the main superplumes underneath Africa and South‐central Pacific are clusters, composed of several individual plumes rather than being a single large mantle upwelling. The iron spin transition in the lower mantle minerals may present a new idea on the origin and the formation of such superplumes, notably sourcing such features in the midmantle. Stagnation of both cold sinking slabs and hot rising plumes can be caused by density and viscosity variation due to the spin transition in iron in ferropericlase (Fp) and a possible spin‐dependent bulk modulus hardening in bridgmanite silicate perovskite (Pv). This process produces intermittent downward spin transition‐induced midmantle avalanches (SIMMA) of the cold sinking flow as well as upward spin transition‐induced midmantle superplume avalanches (SIMMSA) of the rising hot plumes, triggered at the spin transition‐induced thermal boundary layer at around 1600 km depth. Our high‐resolution axi‐symmetric models reveal that the hot upwellings, trapped below ∼1600 km depth, can suddenly penetrate into the upper levels in the mantle and spread laterally for hundreds of kilometres. Owing to the upward penetration of the midmantle‐rooted superplumes, as broad as ∼1500 km across, a large amount of heat can be delivered to the upper mantle and base of the lithosphere with implications for large volcanic episodes.
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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 | 0.000 |
| 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 | 0.001 | 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".