Inferences of Mantle Viscosity Based on Ice Age Data Sets: The Bias in Radial Viscosity Profiles Due to the Neglect of Laterally Heterogeneous Viscosity Structure
Bibliographic record
Abstract
Abstract Inferences of mantle viscosity using glacial isostatic adjustment (GIA) data are hampered by data sensitivity to the space‐time geometry of ice cover. A subset of GIA data is relatively insensitive to this ice history: the Fennoscandian relaxation spectrum (FRS), postglacial decay times in Canada and Scandinavia, and the rate of change of the degree‐2 zonal harmonic of the geopotential ( ). These geographically limited data have been inverted to constrain the radial (one‐dimensional [1D]) mantle viscosity profile. We explore potential biases in these 1D inversions introduced by neglecting a three‐dimensional (3D) viscosity structure. We perform 1D Bayesian inversions of synthetic GIA data generated from Earth models with realistic 3D variations in mantle viscosity and lithospheric thickness and compare results to the 1D viscosity profile associated with the 3D model used to generate the synthetics. Differences between these two 1D profiles reflect GIA data resolution and biasing introduced by neglecting, in the inversions, a 3D viscosity structure. We focus on the second issue, demonstrating that the largest bias occurs within the upper mantle (in particular, the transition zone). This remains consistent when varying inversion parameters (e.g., prior/starting models) and the 1D/3D viscosity fields adopted in generating the synthetics. Inversions of individual data sets show 3D biasing increases for data exhibiting shallower (thus more localized) sensitivity to viscosity. Of the data considered herein, inversions of the FRS are subject to the largest bias followed by decay time data. The bias is minimal for , as its deeper sensitivity is accompanied by broader averaging of structure in radial and lateral directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".