Is the mantle transition zone uniform beneath Precambrian shields?
Bibliographic record
Abstract
To understand the ubiquitous nature of the Precambrian shield, we investigated the structure of the mantle transition zone (MTZ) beneath the Canadian, Brazilian, Baltic, African, and Australian Shields. Receiver Functions were computed from data collected from various stations sampling these regions, and the topography of the MTZ boundaries was mapped using depth-migrated RF images generated with two 3D tomographic velocity models, LLNL_G3D_JPS and GyPSuM. The depth-migrated images from both models reveal a thinner-than-usual MTZ beneath all the Precambrian shields, with an average thickness of approximately 238 ± 8 km. The upper boundary of the MTZ (the 410 km discontinuity) shows distinct topography, while the lower boundary (the 660 km discontinuity) is found at shallower depths. This suggests that the main cause of MTZ thickness variation is the shallowing of the 660 km discontinuity, pointing to a post-spinel transition occurring at higher temperatures with a negative Clapeyron slope, supporting the theory of whole-mantle convection. These results suggest that mantle plumes have appreciable influence the MTZ beneath Precambrian shields, extending to its base, which also gains support (/may also be accounted by) from the global mantle warming observations at the 660 km discontinuity. Further numerical modeling/ simulation studies are needed to test this hypotheses.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".