What Controls Effective Elastic Thickness of the Lithosphere in the Pacific Ocean?
Bibliographic record
Abstract
Abstract The effective elastic thickness ( ) of the lithosphere is a proxy for mechanical strength and can be used to constrain lithospheric rheology and understand how surface deformation relates to deep Earth processes. Here, we map variations over the Pacific Ocean from the inversion of the admittance between free‐air gravity anomaly and bathymetry data calculated using a continuous wavelet transform, taking both surface and subsurface loads into account. The Pacific lithosphere show ranging between 0 and 80 km with a mean of 13.5 km and a standard deviation of 12.3 km. We find that is generally poorly correlated with plate loading age, crustal age, heat flow and Curie point depth, except for relatively young (<60 Ma) and warm lithospheres. Most oceanic plateaus and seamounts show km, with the lowest values (<5 km) around active spreading centers. The highest estimates (>30 km) are found along subduction zones and around the Hawaiian‐Emperor Seamount Chain (HESC). Taken together, these results support a temperature control on for small loads and warm lithosphere through steady‐state creep processes, but strain hardening operating at large plastic strain and low temperature could explain high associated with large‐amplitude and long‐wavelength loads (subduction zones and the HESC) and should be incorporated in yield strength models of oceanic .
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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 | 0.000 |
| 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) | 0.001 | 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".