A new back‐and‐forth iterative method for time‐reversed convection modeling: Implications for the Cenozoic evolution of 3‐D structure and dynamics of the mantle
Bibliographic record
Abstract
Abstract The 3‐D distribution of buoyancy in the convecting mantle drives a suite of convection‐related manifestations. Although seismic tomography is providing increasingly resolved images of the present‐day mantle heterogeneity, the distribution of mantle density variations in the geological past is unknown, and, by implication, this is true for the convection‐related observables. The one major exception is tectonic plate motions, since geologic data are available to estimate their history and they currently provide the only available constraints on the evolution of 3‐D mantle buoyancy in the past. We developed a new back‐and‐forth iterative method for time‐reversed convection modeling with a procedure for matching plate velocity data at different instants in the past. The crucial aspect of this reconstruction methodology is to ensure that at all times plates are driven by buoyancy forces in the mantle and not vice versa. Employing tomography‐based retrodictions over the Cenozoic, we estimate the global amplitude of the following observables: dynamic surface topography, the core‐mantle boundary ellipticity, the free‐air gravity anomalies, and the global divergence rates of tectonic plates. One of the major benefits of the new data assimilation method is the stable recovery of much shorter wavelength changes in heterogeneity than was possible in our previous work. We now resolve what appears to be two‐stage subduction of the Farallon plate under the western U.S. and a deeply rooted East African Plume that is active under the Ethiopian volcanic fields during the Early Eocene.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".