Dynamic Topography and Ice Age Paleoclimate
Bibliographic record
Abstract
The connection between the geological record and dynamic topography driven by mantle convective flow has been established over widely varying temporal and spatial scales. As observations of the process have increased and numerical modeling of thermochemical convection has improved, a burgeoning direction of research targeting outstanding issues in ice age paleoclimate has emerged. This review focuses on studies of the Plio-Pleistocene ice age, including investigations of the stability of ice sheets during ice age warm periods and the inception of Northern Hemisphere glaciation. However, studies that have revealed nuanced connections of dynamic topography to biodiversity, ecology, ocean chemistry, and circulation since the start of the current ice-house world are also considered. In some cases, a recognition of the importance of dynamic topography resolves enigmatic events and in others it confounds already complex, unanswered questions. All such studies highlight the role of solid Earth geophysics in paleoclimate research and undermine a common assumption, beyond the field of glacial isostatic adjustment, that the solid Earth remains a rigid, passive substrate during the evolution of the ice age climate system. ▪ Dynamic topography is the large-scale, vertical deflection of Earth's crust driven by mantle convective flow. ▪ This review highlights recent research exploring the implications of the process on key issues in ice age paleoclimate. ▪ This research includes studies of ice sheet stability and inception as well as inferences of peak sea levels during periods of relative ice age warmth. ▪ This review also includes studies on longer timescales, continental-scale ecology and biodiversity, the long-term carbon cycle, and water flux across oceanic gateways.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".