Predicting deglacial stratigraphy following Snowball Earth with 3D forward modelling
Bibliographic record
Abstract
Rapid eustatic rise during deglaciations should cause sedimentary condensation and depositional hiatuses on marine shelves. Determining the duration of these hiatuses is challenging, especially in sequences that cannot be reliably dated. Recently, it was suggested that a global prolonged hiatus could have ensued following the Neoproterozoic Snowball Earth events. However, the duration and stratigraphic characteristics of these events are uncertain. Here, we utilize 3D stratigraphic forward modelling software DionisosFlow to 1) estimate its duration following a Snowball Earth when considering 800 m glacioeustatic rise over 40 kyr, and 2) explain the stratigraphic fingerprint of such an event. We tested several margin configurations and different sediment flux scenarios; our findings indicate that the duration of the hiatus, as predicted, will increase with accommodation, and decrease with sediment supply. Simulating an average (modern) glaciated margin with an average sediment flux results in prolonged sediment starvation on the outer shelf, lasting over 6 Myr. More complex models show how topography, sediment type, and sediment volume during and after the deglaciation affect the stratigraphic record. We compare the predicted model outputs with observed Snowball Earth stratigraphy from the Kimbereley region of NW Australia to reconstruct the paleoenvironmental conditions. This work demonstrates how 3D stratigraphic modeling can help clarify deglacial stratigraphy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".