Playing with Edges: The Influence of Arbitrary Definitions on Hotspot–LLSVP Correlations
Bibliographic record
Abstract
Correlating surface hotspot volcanism with sharply defined edges of Large Low Shear Velocity Provinces (LLSVPs) is a common yet potentially oversimplified approach in mantle geodynamics. Such direct radial projections ignore the lateral displacement of plume conduits observed in seismic tomographic imaging, which suggests that purely vertical transport through the mantle is not guaranteed. Furthermore, many studies merge the African and Pacific LLSVPs, despite evidence that their correlation with hotspots differs significantly. These oversimplifications can lead to misinterpretations of plume-lithosphere interactions, the interaction between mantle plumes and the ambient ”mantle wind”, and mantle flow dynamics in general. Here, we systematically investigate how varied criteria can alter the inferred hotspot–LLSVP edge relationship. We separately analyze African and Pacific LLSVPs using: multiple tomography models, horizontal-gradient based definitions of edges, different vote-map methodologies, and distinct plume geometry assumptions–from perfectly vertical “spokes” to randomly deflected trajectories. We also apply the Back-and-Forth Nudging (BFN) method applied to time-reversed thermal convection, initialized with a present-day seismic–geodynamic–mineral physics model (Glisovic & Forte, 2016), to provide a geodynamically consistent assessment of the relationship between present-day hotspot locations and their source regions in the deep lower mantle. This independent geodynamic assessment clarifies how arbitrary choices concerning the interpretation of hotspots and LLSVP edges may lead to biased or skewed deep-plume reconstructions. Our results reveal that adjustments in hotspot catalogs, or the decision to combine the two main LLSVPs rather than regard each as dynamically distinct, can yield important differences in the significance attributed to sharply defined LLSVP edges. These findings underscore that commonly cited correlations between hotspot locations and LLSVP boundaries hinge on assumptions that vary considerably across the literature. Recognizing and rigorously defining input parameters–particularly the separate treatment of the African and Pacific LLSVPs and the inclusion of realistic lateral plume deflection–proves essential for robust interpretations of deep Earth structure. This highlights the need for standardized methodologies and careful parameter choices to avoid overstating the importance of LLSVP edges in shaping plume pathways.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".