GIA-based sea level change due to Marinoan snowball Earth deglaciation
Bibliographic record
Abstract
Marinoan snowball Earth offers us a set of sedimentary and geochemical records for exploring glacial isostatic adjustment (GIA) associated with one of the most severe glaciations in Earth history.An accurate prediction of GIA-based relative sea level (RSL) change associated with a snowball Earth meltdown will help to explore sedimentary records for RSL changes and to place independent constraints on mantle viscosity and on the durations of syn-deglaciation (T d ) and cap carbonate deposition.Here we mainly examine post-deglacial RSL change characterized by an RSL drop and a resumed transgression inferred from the cap dolostones on the continental shelf in South China.Such a non-monotonic RSL behavior may be a diagnostic GIA-signal for the Marinoan deglaciation resulting from a significantly longer post-deglacial GIA-response than that for the last deglaciation.A post-deglacial RSL drop followed by transgression in South China, which is significantly affected by Earth's rotation, is predicted over the continental shelf for models with T d 20 kyr and a deep mantle viscosity of 5 10 22 Pa s regardless of the upper mantle viscosity.The inferred GIA model also explains the post-deglacial RSL changes such as sedimentary-inferred RSL drops on the continental shelf in northwestern Canada and California at low-latitude regions insignificantly affected by Earth's rotation.Furthermore, the good match between the predicted and observed RSL changes in South China suggests an approximate duration of 50 kyr for the Marinoan 17 O depletion event, an atmospheric event linked to the post-Marinoan drawdown of CO 2 and the concurrent rise of O 2 .
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".